Spatio temporal variability of soil temperature under different coverage in Pernambuco state semiarid

Authors

  • Alan Cézar Bezerra Universidade Federal Rural de Pernambuco
  • Abelardo Antônio de Assunção Montenegro Universidade Federal Rural de Pernambuco
  • Héliton Pandorfi Universidade Federal Rural de Pernambuco
  • José Roberto Lopes da Silva Universidade Federal Rural de Pernambuco
  • Carolyne Wanessa Lins de Andrade Universidade Federal Rural de Pernambuco
  • Wellington Pereira da Silva Universidade Federal Rural de Pernambuco

Keywords:

geostatistics, kriging, semivariogram

Abstract

Soil temperature is a great importance property for the development of plants and soil microorganisms, with direct influence on water supplies, carbon and the final crop yield. Associated with the scenery of the semiarid northeast, high incidence of solar radiation and irregular rainfall, there is a condition of high thermal stress for plants. Therefore, this study was conducted in order to determine the spatial variability of surface soil temperature and 15 cm deep, in the municipality of Pesqueira, semiarid region of Pernambuco. Infrared and digital thermometers with 15 cm long were used to measure the temperature at 65 points in an area of 3952 m², being divided into: an area 576 m² cultivated with irrigation carrot, composing a record mesh equidistant every 4 m, totaling 49 points; Another area of 3376 m² with bare soil and 16 points divided into a regular grid of 12 m and a linear sampling distant from each other 24 m. It was verified that there is a delay in the soil temperature with depth, so that the temperature propagates through layers of soil, providing different times for each soil layer to reach the maximum daily temperature. The recording of data was performed on November 13, 2012, at different times: 8:30, 10:30, 15:00 and 17:00 h. It was thermal lag of the temperature in the layer of 0-15 cm deep, giving a differentiation between the timestamp for the temperature to reach the maximum daily. It was noted strong spatial dependence to surface temperature across the area evaluated, which is justified by ground cover. The Gaussian model was presented that best fit the different times and depths, especially for the area with vegetation.

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Published

2016-12-30

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