Ecological niche models of productive corn races under climate change scenarios in central‐eastern Mexico

Author:

Estrada‐Contreras Israel1,Pavón Numa P.1ORCID,Cadena Jessica Bravo1,Bourg Amandine2

Affiliation:

1. Centro de Investigaciones Biológicas Universidad Autónoma del Estado de Hidalgo Pachuca Hidalgo México

2. Instituto de Ecología, A.C., Carretera antigua a Coatepec 351, Xalapa Veracruz 91070 México

Abstract

AbstractCorn (Zea mays L.) is the basis of the Mexican diet. In this country, corn is produced by about 2.5 million small farmers and grown on 8.4 million hectares in rainfed agriculture. However, its production is not enough to meet demand. Climate change may negatively impact corn production, putting Mexico's food security at risk. The main goal of this work was to identify which corn races can find favorable environmental conditions in the future under climate change scenarios in the Sierra Madre Oriental. The Sierra Madre is a region highly vulnerable to climate change; in 2020, corn production was 5,785,561 Mg yr−1 of which 49.87% was from rainfed agriculture. These corn races could be proposed to replace others not tolerant to climate change, mainly at high altitudes. Potential distribution models were obtained for seven highly productive maize races. The projections were generated with the MaxLike algorithm using representative concentration pathways 4.5 and 8.5 scenarios to 2050 and 2070. In addition, general circulation models (CCSM4, GISS.E2‐R, and MPI‐ESM‐LR) were used. Our results show that the Tabloncillo and Tuxpeño races could slightly increase their potential distribution in the future. Both corn races present the highest number of municipalities with adequate environmental conditions for their future presence. As a strategy for adaptation to climate change, it is proposed to replace or complement maize races in high‐altitude areas with other tropical ones with greater tolerance to high temperatures, such as the Tuxpeño race.

Publisher

Wiley

Subject

Agronomy and Crop Science

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