Evaluation of the Effects of Climate Change on Agricultural Production - La Rinconada Town Center – Ancash, Peru

Abstract

The objective of this research is to evaluate the effects of climate change on agricultural production in the town of Rinconada in the district of Santa, Ancash, Peru. Climate change variables (Precipitation and Temperature) vs. agricultural production were analyzed. We worked with the information collected from the SENAHMI (Servicio Nacional de Methodology e Hydrology del Peru), Lima -Peru through the database of the Pisco station, the Santa Users Board, the La Rinconada Irrigation Commission and the integrated system of agrarian statistics of the Ancash region, Peru [20]. The perception of the irrigation commission of the town of La Rinconada, district of Santa, Ancash, Peru was also evaluated through a survey with questions about the effects of the 2 variables of climate change: precipitation and temperature vs agricultural production of the main crops: corn, cotton, and rice. Climate change was modeled between the periods of 2006 and 2010 and between 2011-2015, through the variables of maximum precipitation and average precipitation in comparison with the variables of planting area, yield, and agricultural production [22]. It can be concluded that it is possible to predict the planting, yield and production of the crops evaluated: corn, cotton, and rice, using only the variables of maximum rainfall, and average rainfall, and that it is necessary to keep investigating the presence of transgenic crops in the Rinconada valley.

Impact of Climate Change and the El Niño – Southern Oscillation on Extreme Rainfall

“…The second analysis in step 2 is the development of local monthly rainfall models based on climate change and ENSO variables. Based on the definition and interpretation of the correlation coefficient [7], the authors consider only variables with a correlation coefficient of at least 60% with the local monthly rainfall. Next in step 3 is the interpretation and discussion of the impacts based on the regression model obtained in step 2.…”

“…And LB = µ -(1.64*σ) (7) with: UB is the Upper Bound, LB is the Lower Bound, µ is the average of data, σ is the standard deviation of data. In this paper, the authors define the status of extremely wet if the amount of rainfall is larger than the upper bound.…”

“…Climate change and the ENSO affect many countries around the world. Problems caused by extreme weather and climate change, such as disease outbreaks, health problems, and high waves, to farmers who fail to harvest, and other social vulnerabilities are becoming more serious [1][2][3][4][5][6][7]. The high rainfall intensity causes the area to be vulnerable to flooding.…”