Livestock Research for Rural Development 28 (11) 2016 | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
Although the Mantaro Valley isn’t one of the main dairy areas of Peru, it has an accelerated dynamic growth in milk production, achieving twice of its production in eight years. However, for this growth to continue the producer must adapt to climate change, because dairy farming depends largely on weather conditions. It is known that adaptation is a two-step process: first the producer must perceive that the climate has changed and then respond to changes through adaptation. Therefore, this research seeks to explore the dairy farmer’s perception in order to have a basis for developing adaptation measures.
The results show that dairy farmers perceive that the climate is changing. In addition, it was shown in this study that despite high levels of perception of changes in climate, the level of knowledge of climate change as a phenomenon is low.
Keywords: adaptation, climate risk, dairy producers, dairy cattle
The effects of climate change are significant in Latin America and the Caribbean because of the variability and the climate extremes in the region (Magrin et al 2007; FAO 2007). Between them, Peru is one of the countries most affected due to the impact of hydro- meteorological phenomena.
These phenomena, such as drought, heavy rains and floods have increased more than six times from 1997 to 2006 and extreme weather events such as landslides, floods, frost and El Niño phenomenon are occurring with greater frequency and intensity (IPCC 2007).
In developing countries such as Peru, agriculture and livestock are the most sensitive economic sectors to climate change (AMCEN 2011). The same observation has been reported by the World Food Programme (2009) who point out that rural communities in developing countries especially women, children and marginalized communities are at greater risk of climate change impacts due to high exposure to natural hazards, its direct climate -dependent resources such as plants, animals, water and land and their limited ability to adapt and cope to climate change.
In our country, raising cattle is a fundamental activity for regional development as it capitalizes for producer, is a source of savings and income, fixed the producer to the field, creates jobs and is one of the few agricultural activities that can be developed in many natural regions of the country (Santa Cruz et al 2006). It also involves 39% of the population, uses 35% of the country and focuses mainly on the highland, where there are the 73% of cattle in the country and 90% is under the management of small producers (CENAGRO 2012).
It is known that adaptation is a two-step process: first the producer must perceive that the climate has changed and then respond to changes through adaptation (Deressa et al 2008, Patt and Schroter 2008). In that sense, Retamal et al (2011) and Fosu - Mensah et al (2010) mentioned that research on climate change requires a line of study that addresses the perception of those involved, since the successful implementation of any strategy requires understanding the sensitivity level, information and understanding on climate change by those who adopt adaptation strategies, so that perception and awareness about climate change identifies what can be done, how, and by whom.
Several studies in perception of rural populations have been performed worldwide, however, none has been held in Peru at the level of dairymen, so this paper seeks to explore the perception to climate change of producers dedicated to dairy farming in the Mantaro Valley, Junin.
The Mantaro valley is located in the center of Peru, between Western and Central Highlands of the Andes at an average altitude of 3330 meters above sea level. It is composed of four provinces: Chupaca, Concepción, Huancayo and Jauja, within 57 districts in total covering a dimension of 53 km long, being the narrowest part of 4 km and the widest part of 21 km approximately (IGP 2012). In Figure 1, the Mantaro Valley divided by districts is shown.
Figure 1. Map of the study area |
For conducting the surveys, agricultural units that meet the following four requirements were selected: having cattle, less than 20 animals (small and medium farmers), produce milk and are over 30 years. The selection procedure was as follows.
From the Fourth National Agricultural Census (CENAGRO 2012), it was found that there are 17010 agricultural units (AU) that have cattle distributed in 57 districts belonging to the Mantaro Valley. Of all those agricultural units, they were selected the ones who have less than 20 cattle (98.8%) getting a total of 16811 AU. Then, agricultural units that produce milk (52.3%) were selected to give a total of 8824 AU. Finally agricultural producers older than 30 years (93.4%) were selected, getting a final total number of 8199 AU.
The study sample (701 AU) represents the 8% of the estimated population, which was calculated with the finite population formula with 95% of confidence. The numbers of surveys per district were calculated in related to the number of dairy producers per district.
The statistical analysis consisted in the application of descriptive and multivariate statistics techniques. The statistical program used was IBM SPSS Statistics Base 22.0.
To determine the classification of level of knowledge about climate change, a cluster analysis (Scott and Knott, 1974) of four variables (knowledge about climate change, its causes, consequences and ways to reduce the impact of climate change) of the total of respondents was conducted, where two clusters were obtained, the second cluster is considered as the group of producers who have no knowledge about climate change. Following this cluster, the level of knowledge of climate change is classified by district. Finally, a hierarchical cluster analysis is performed to classify the districts in three groups: high knowledge, medium knowledge and low knowledge of climate change.
The 99.3% of the sample said that the climate has changed. As shown in Figure 2, more than half of the producers (61.1%) attributes the change in climate as a recent process, but about a quarter of the sample (24.7%) said that this is a process which it has been generating more than thirty years ago.
Figure 2. Identification the number of years since the weather has begun to change |
In Figure 3, it is observed that the perception of the producer is accentuated in four main indicators: summer temperature increase, winter temperature increase, reduced the intensity of frost and reduced production of soils.
Figure 3. Main indicators of climate change |
The 68.76% of the sample have ever heard the term climate change on television, radio, neighborhood talks, training or from their children.
In Figure 4, it shows that 73.91% of the sample know the causes of climate change, 76.14% of the sample know the consequences of climate change and 68.13% of the sample know the ways to reduce climate change effects. Related to the knowledge about climate change, it is observed that 17.82% of the sample does not know the ways to reduce climate change effects.
Figure 4.
Percentage of producers who know the causes, consequences and ways to reduce
the impacts of climate change in relation to their level of knowledge |
In Table 1, it is observed the classification through a Cluster Analysis, depending on the level of knowledge of climate change from the 701 producers, where two clusters are obtained. Cluster 2 is identified as producers who have no knowledge about climate change.
Table 1. Two-step cluster features |
|||
Variables |
Cluster 1 |
Cluster 2 |
|
Know the causes |
Much |
22.7 |
0 |
Less |
73.8 |
0 |
|
Nothing |
3.51 |
100 |
|
Know the consequences |
Much |
19.4 |
0 |
Less |
75.8 |
0 |
|
Nothing |
4.75 |
100 |
|
Know the ways to reduce it |
Much |
13.8 |
0 |
Less |
67.2 |
0 |
|
Nothing |
17.6 |
100 |
|
The 57 districts were classified through a hierarchical cluster considering percentage of producers who have knowledge about climate change (CC) as the variable of classification (CC). Related to this variable, the cluster 1 that has 84.5% of dairy producers in the sample who have knowledge of CC was considered as group of dairy producers with a high knowledge of climate change, the cluster 2 that has 65.9% of dairy producers in the sample who have knowledge about CC was considered as a group of dairy producers with a medium knowledge of climate change and the cluster 3 with a 48.8% of dairy producers who have knowledge of the CC was considered as group of dairy producers with a low knowledge of CC.
Through the classification in the step before districts were classified in relation to the high (cluster 1), medium (cluster 2) and low (cluster 3) knowledge on climate change, as shown in Table 2.
Table 2. District classification according to the level of knowledge of climate change |
||
High knowledge |
Medium knowledge |
Low knowledge |
Acolla |
Ahuac |
Cullhuas |
Apata |
Ataura |
El Tambo |
Chongos Bajo |
Chilca |
Huancán |
Chupaca |
El Mantaro |
Huayucachi |
Chupuro |
Huacrapuquio |
Huertas |
Concepción |
Hualhuas |
Jauja |
Huachac |
Huamancaca |
Muquiyauyo |
Huamalí |
Manzanares |
Nueve de Julio |
Leonor Ordoñez |
Masma |
Pancán |
Huancayo |
Matahuasi |
Pilcomayo |
Huaripampa |
Molinos |
San Jerónimo de Tunán |
Julcán |
Muqui |
Sapallanga |
Marco |
Pucará |
Tres de Diciembre |
Mito |
San Agustín de Cajas |
Tunanmarca |
Orcotuna |
San Juan de Iscos |
Viques |
Paca |
San Lorenzo |
|
Quichuay |
San Pedro de Saño |
|
Quilcas |
Sausa |
|
San Pedro de Chunán |
Sicaya |
|
Santa Rosa de Ocopa |
Sincos |
|
Yauli |
||
Yauyos |
||
The results presented in this study, in relation to the perception of climate change are similar to the work done by Ulloa and Yager (2007) in a community of producers of camelids in Bolivia, who obtained that 100 percent of its respondents recognize that the climate is changing. Similarly, Chuncho (2011) obtained a high percentage of dairy farmers (71%) which recognizes climate changes in the area of Rio Blanco and Paiwas in Nicaragua.
It is observed that the perception of the producer is accentuated in four main indicators: increase in summer temperature, increase in winter temperature, reduced the intensity of frost and reduced soil production. Similar results obtained Ulloa and Yager (2007) in Bolivia, where the main indicators are sudden changes in temperatures, frost and winds. Meanwhile, Olmos et al (2013) report the loss of crops as an indicator and Fosu -Mensah (2010) mentions temperature and rainfall as leading indicators of climate change.
With regard to temperature, 77.6% and 76.8% of the sample indicate that the temperature in winter and summer temperatures respectively increased, which coincides with the point made by Avalos et al (2011) who claim that in the Mantaro valley, the average temperature tends to increase, as would be expected to global warming.
Regarding the number of freezing weather, 51.1% of the sample said that has increased and 27.8% of the sample points that has been reduced. With respect to the intensity of frost the 76.8% of the sample points that have been reduced and 9.70% of the sample said that they have increased. This trend match with the report of Avalos et al (2011), who point out that in general the frequency of frosts is decreasing, however there are some places with opposite trend, this trend justified why the perception of the frequency is divided in relation to frost values as one of the main indices of climate change.
In relation to rains, it is observed that 38.8% of the sample said that the rains have increased and 42.2% of the sample said they has been reduced, but in relation to the duration of the rainy season , the 35.9% notes that it has increased and 39.9% notes that it has been reduced. These results agree with those reported by Avalos et al (2011) who mentioned that precipitation generally show a tendency to decrease in the Mantaro valley. However, the decline rain is accentuated in some places (-17%), while in the south of the basin is increased by up to 23%. This trend is also associated with the decreased in the intensity of rainfall and increased the number of consecutive dry days. Which justified why the perception of the producer regarding rainfall is similar in both increase and decrease.
AMCEN (African Ministerial Conference on Environment) 2011 Addressing Climate Change Challenges in África: A Practical Guide Towards Sustainable Development. From: http://www.unep.org/roa/amcen/docs/publications/guidebook_CLimateChange.pdf
Avalos G, Cubas F, Oria C, Díaz A, Quispe N, Rosas G, Cornejo A, Solís O and Guerra S 2011 Atlas Climático de Precipitación y Temperatura del Aire en la Cuenca del Río del Mantaro. Servicio Nacional de Meteorología e Hidrología del Perú – SENAMHI. From: http://www.met.igp.gob.pe/publicaciones/2000_2007/Atlas_Climatico.pdf
CENAGRO (Censo Nacional Agropecuario) 2012 Resultados preliminares. Instituto Nacional de Estadística e informática. Perú. From: http://censos.inei.gob.pe/cenagro/tabulados/
Chuncho C 2011 Análisis de la percepción y medidas de adaptación al cambio climático que implementan en la época seca los productores de leche en Río Blanco y Paiwas, Nicaragua. Centro Agronómico Tropical de Investigación y Enseñanza. Tesis para optar el título de Magister Scientiae en Agricultura Ecológica. From: http://repositorio.bibliotecaorton.catie.ac.cr/handle/11554/312
Deressa T, Hassan R, Alemu T, Yesuf M and Ringler C 2008 Analyzing the Determinants of Farmers’ Choice of Adaptation Methods and Perceptions of Climate Change in the Nile Basin of Ethiopia. International Food Policy Research Institute (IFPRI) Discussion Paper No. 00798. Environment and Production Technology Division, Washington D.C. From: http://core.ac.uk/download/files/153/6337745.pdf
FAO (Organización de las Naciones Unidas para la agricultura y la alimentación) 2007 Cambio climático y seguridad alimentaria: un documento marco. Grupo de trabajo interdepartamental de la FAO sobre el cambio climático. FAO, Rome-Italy. From: http://bvssan.incap.int/local/cambio-climatico/CAMBIO-CLIMATICO-INSAN-MARCO-FAO.pdf
Fosu-Mensah B, Vlek P and Manschadi A 2010 Farmers’ perception and adaptation to climate change: a case study of Sekyedumase district in Ghana. Tropentag, September 14-16, Zurich. From: http://www.tropentag.de/2010/abstracts/full/203.pdf
IGP (Instituto Geofísico del Perú) 2012 Manejo de riesgos de desastres ante eventos meteorológicos extremos en el Valle del Mantaro. Resultados del proyecto “Manejo de riesgos de desastres ante eventos meteorológicos extremos (sequías, heladas y lluvias intensas) como medida de adaptación ante el cambio climático en el valle del Mantaro-MAREMEX”. From: http://www.met.igp.gob.pe/proyectos/maremex/
IPCC (Panel Intergubernamental del Cambio Climático) 2007 Cambio climático 2007: Informe de síntesis. Contribución de los Grupos de trabajo I, II y III al Cuarto Informe de evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático. Ginebra-Suiza, 104 pp. From: http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/ar5_wgII_spm_es.pdf
Magrin G, Gay C, Cruz D, Giménez J, Moreno A, Nagy G, Nobre C and Villamizar A 2007 Latin America Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 581-615. From: https://www.ipcc.ch/pdf/assessment-report/ar4/wg2/ar4_wg2_full_report.pdf
Olmos E, González M and Contreras M 2013 Percepción de la población frente al cambio climático en áreas naturales protegidas de Baja California Sur, México. Revista Latinoamericana Polis. From: http://polis.revues.org/9158
Patt A and Schröter D 2008 Climate risk perception and challenges for policy implementation: evidence from stakeholders in Mozambique. Global Environmental Change, 18, 458-467.
Retamal R, Rojas J and Parra O 2011 Percepción al cambio climático y a la gestión del agua: aportes de las estrategias metodológicas cualitativas para su comprensión. Ambiente & Sociedade, Brasil 4(1):175-194. From: http://www.scielo.br/scielo.php?script=sci_arttext&pid= S1414-753X2011000100010
Santa Cruz V, Sánchez M and Pezo S 2006 Análisis de la cadena productiva de lácteos de Cajamarca - Plan estratégico de la cadena de productos lácteos. MINAG 2004. From: http://www2.congreso.gob.pe/sicr/cendocbib/con3_uibd.nsf/36DFC5F97808BDCB052579810054F1BF/$FILE/218.pdf
Scott A and Knott M. 1974. A cluster analysis method for grouping means in the analysis of variance. Biometrics 30, 507-512.
Ulloa D and Yager K 2007 Cambio Climático: Percepción local y Adaptaciones en el Parque Nacional Sajama. Memorias del Taller Cambio Climático, Sajama-Bolivia. From: http://www.cambioclimatico-bolivia.org/archivos/20120311235557_0.pdf
WFP (World Food Programme 2009 Climate Change, Food Insecurity and Hunger: Key Messages for UNFCCC Negotiators - Technical Paper for the IASC Task Force on Climate Change. 8pp. From: http://www.careclimatechange.org/files/reports/IASC_CC_FS.pdf
Received 13 April 2016; Accepted 6 October 2016; Published 1 November 2016