Climate variability and agricultural productivity in areas with erratic rainfall patterns

Authors

  • Ignacio Sánchez Cohen Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134
  • Marco Antonio Inzunza Ibarra Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134
  • Ernesto Alonso Catalán Valencia Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134
  • José Luis González Barrios Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134
  • Guillermo González Cervantes Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134
  • Miguel Velásquez Valle Centro Nacional de Investigaciones Disciplinarias en Relaciones Agua Suelo Planta Atmósfera, INIFAP. Canal Sacramento km 6.5, Gómez Palacio Durango México. C. P. 35140. Tel. 01 871 7191076. Fax. 01 871 7191134

DOI:

https://doi.org/10.29312/remexca.v3i4.1433

Keywords:

climate uncertainty, modeling, risk

Abstract

The high variability in space and time of the rainfall patterns, make agriculture in rainfed areas subject to climatic risk. In this situation, the best tool to support decision-making is the hydro-climatic modeling, where the hydrological stochastic processes are considered. In the present study, nested series of algorithms (AA) are used in order to estimate maize crop yield under different climate scenarios. The algorithm is calibrated and applied to a poor rainfed region in northern Mexico (Cuencamé, Durango). It is part of a weather generator (WXPARM) for climate parameters that define the region later to quantify the impact of maize yield under climatic change conditions; using a rescaled model to apply global climatic data models (GCMs) at plot level (SDM) and finally the matrices that define the monthly weather conditions in the region of study are used in a model to assess the impact on yield (EPIC) by modeling the balance of moisture in the soil. The results indicate that under climatic change scenarios, it is expected a yield increases of up to 0.3 t ha-1 as the change in expected weather patterns, expecting a bimodal behavior. According to the weather patterns in the future, it might be considered to adjusting planting dates for the maximum crop requirements coinciding with the presence of rain.

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Published

2018-07-06

How to Cite

Sánchez Cohen Ignacio, Marco Antonio Inzunza Ibarra, Catalán Valencia Ernesto Alonso, González Barrios José Luis, González Cervantes Guillermo, and Velásquez Valle Miguel. 2018. “Climate Variability and Agricultural Productivity in Areas With Erratic Rainfall Patterns”. Revista Mexicana De Ciencias Agrícolas 3 (4). México, ME:805-11. https://doi.org/10.29312/remexca.v3i4.1433.

Issue

Vol. 3 No. 4 (2012)

Section

Investigation notes

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