Revista de la Facultad de Ciencias
Agrarias. Universidad Nacional de Cuyo. En prensa. ISSN (en línea) 1853-8665.
Original article
Effects
of climate change on nine rainfed Zea mays races in Chiapas, Mexico
Efectos
del cambio climático sobre nueve razas de temporal de Zea mays en
Chiapas, México
Alejandro Vázquez
Moreno1,
Carolina Orantes
García4,
Eduardo Espinoza
Medinilla4
1 Universidad de Ciencias y Artes de Chiapas. Facultad de
Ingeniería. Programa de Maestría en Ciencias en Desarrollo Sustentable y
Gestión de Riesgos. Tuxtla Gutiérrez. Libramiento Norte Poniente N° 1155.
Colonia Lajas Maciel. C. P. Chiapas. México.
2 Universidad de Ciencias y Artes de Chiapas. Facultad de
Ingeniería.
3 Oikos. Conservación y Desarrollo Sustentable AC. Calle
Bugambilias N° 5. Colonia Bismark. C. P. 29267. San Cristóbal de las Casas.
Chiapas. México.
4 Universidad de Ciencias y Artes de Chiapas. Instituto de
Ciencias Biológicas.
* tamararioja@gmail.com
Abstract
Maize cultivation (Zea
mays) is essential for Mexico from a nutritional, cultural and economic
perspective. Scientific literature ignores the impact of anthropogenic causes
of climate change on rainfed cultivation of Z. mays in Chiapas, Mexico,
one of the poorest states in the country. Therefore, we modeled the feasibility
of rainfed cultivation for nine races of rainfed maize for the years 2060 and
2100. The MaxEnt 4.4.4 algorithm modeled maize cultivation under two scenarios
(4.5 and 8.5) for 2060 and 2100. Model inputs were 12 bioclimatic variables, 3
climatic variables, and 1 elevation variable. All layers were obtained from the
WorldClim 2.1 project. By 2060, the suitable area for rainfed cultivation of
the nine Z. mays races would drastically decrease under both modeled
scenarios. By 2100, this area will decrease for seven races, and disappear for
the Olotillo and Olotón races. To the best of our knowledge, this is the first
study providing fundamental information on how climate change will negatively
impact the nine Z. mays races in Chiapas, Mexico. This enables the
development of sustainable management protocols or conservation strategies.
Keywords: bioclimatic
variables, climatic variables, elevation, MaxEnt 4.4.4, rainfed maize races
Resumen
El cultivo de maíz
(Zea mays) es de gran importancia para la población de México desde una
perspectiva nutricional, cultural y económica. La literatura científica carece
de información sobre el impacto del cambio climático antropogénico en el
cultivo de temporal de Z. mays en Chiapas, México, uno de los estados
más pobres del país. Es por ello que modelamos la viabilidad del cultivo de
temporal para nueve razas de maíz de temporal para los años 2060 y 2100. Se
utilizó el algoritmo MaxEnt 4.4.4 para modelar bajo dos escenarios (4.5 y 8.5)
para el 2060 y el 2100. El modelo se alimentó con 12 variables bioclimáticas, 3
variables climáticas y una variable de elevación. Todas las capas se obtuvieron
del proyecto WorldClim 2.1. Se proyecta que el área adecuada para el cultivo de
temporal de las nueve razas de Z. mays disminuirá drásticamente para
2060 bajo los escenarios 4.5 y 8.5. Para el año 2100, bajo los mismos
escenarios, la superficie se reducirá para siete razas, mientras que en las
razas de maíz Olotillo y Olotón desaparecerá por completo. Por primera vez, se
proporciona información fundamental sobre cómo el cambio climático impactará
negativamente a las nueve razas de Z. mays en Chiapas, México, lo que
permitirá desarrollar protocolos de manejo sustentable y/o estrategias de
conservación.
Palabras clave: variables
bioclimáticas, variables climáticas, elevación, MaxEnt 4.4.4, razas de maíz de
temporal
Originales: Recepción: 30/07/2024 - Aceptación: 14/04/2025
Introduction
Agriculture is the
primary source of global food supply (7). However, this
activity is severely threatened by anthropogenic causes of climate change,
especially for rainfed crops (21). Increases in
environmental temperature, changes in precipitation patterns, and drought
events often reduce crop production area (34).
Maize (Z. mays) constitutes
an essential food in Mexico. It is a highly nutritious source consumed in
various presentations, holding particular socio-cultural value for indigenous
people and the whole population (24, 32).
Maize production is
relevant for national and international markets (44). However, in
southeastern Mexico, most rainfed maize areas are being abandoned given social
issues, changes in land use, poor technological implementation, and inadequate
public policies (55). In Chiapas,
farmers are vulnerable to these changes. Aimed at sustaining rainfed maize
production, many have resorted to hybrid seeds, declining native varieties and
losing ancestral techniques in favor of contemporary ones (31).
Mexico’s maize
races are cataloged in 7 racial groups based on morphology, adaptation types,
and genetic traits (20, 51, 52). Within these
groups, the National Commission for Knowledge and Use of Biodiversity
(2020)
reports a total of 64 races, 59 of which are native. In Chiapas, 11 races have
been documented, with 9 having geographical records associated with rainfed
agriculture: Zapalote Chico, Cubano Amarillo, Tepecintle, Zapalote Grande,
Tuxpeño, Vandeño, Comiteco, Olotón and Olotillo (10), specimens
registry of the National Biodiversity Information System (www.snib.mx/).
No study has yet analysed the impact of climate change on
cropping areas for these maize races in the state of Chiapas, Mexico. This
information is crucial considering approximately 88% of total maize production
in Chiapas depends on rain cycles (30). Each race is
cultivated in distinct environmental conditions, adapted to specific
temperature, precipitation, and elevation (3,
6, 11, 36, 56). Therefore, this study aims to determine whether these races
will be differently affected by climate change and provide insights for sustainable
management plans (49, 57). This, considering
reproductive and adaptation strategies, and cultural practices preventing their
disappearance (33).
Materials
and methods
Study
area
The study area
encompassed the entire state of Chiapas, located in southeastern Mexico. It
borders the state of Tabasco to the north, Veracruz and Oaxaca to the west, the
Pacific Ocean to the south, and Guatemala to the east. It spans 74,415 km² (13) and lies between
17°59’ to 14°32’ N and 90°22’ to 94°14’ W.
Annual
precipitation varies widely across the state, ranging from 800 to 2,500 mm.
Variations in the north range from 1,500 to 2,500 mm and between 1,500 and
2,000 mm in the south. In the central part of the state, most areas register
annual precipitation between 800 and 1,200 mm, while in the rest of the state,
precipitation ranges from 1,200 to 1,500 mm (54).
Chiapas held the
first place in harvested maize area from 2010 to 2021. However, now the state
ranked 6th in national production
volume with 1.3 million tons (45).
Database
of spatial records for nine maize races (Z. mays) in Chiapas
Georeferenced
records for the nine maize races were obtained from scientific databases such
as the 2023 Geoinformation Portal from the National Commission
for the Knowledge and Use of Biodiversity (2023), the Biodiversity
Information Facility (www.gbif.org/es/), COMPADRE (https://compadre-db.org/;
COMPADRE Plant Matrix Database 2023), and the Specimen Register of the National
System of Information on Biodiversity (www.snib.mx/).
After removing all
duplicated records or those with erroneous coordinates (42), we defined 1,215
records for the Comiteco race, 60 for Cubano Amarillo, 209 for Olotillo, 456
for Olotón, 77 for Tepecintle, 1,159 for Tuxpeño, 63 for Vandeño, 13 for Zapalote
Chico, and 40 for Zapalote Grande, totaling 3,292 presence records.
Finally, using the
“spThin” package in R software and RStudio, the spatial correlation among the
presence data for the nine maize races was reduced through 100 iterations (2,
47, 50).
Environmental
layers for the nine rainfed maize races
Environmental
layers for minimum, maximum, and average monthly temperatures, monthly
cumulative rainfall, elevation and 19 bioclimatic variables were obtained to determine
suitable environmental conditions for the nine rainfed maize races in Chiapas.
These layers correspond to cultivating periods for each race in Chiapas. The
cropping season for the Zapalote Chico race occurs from May (planting) to
August (harvest) (3, 28, 36). In contrast,
Cubano Amarillo, Tepecintle, Vandeño, Tuxpeño, and Zapalote Grande extend from
May to October (11), while Comiteco,
Olotón, and Olotillo races grow from May to December (3,
11).
All layers were
obtained from the WorldClim 2.1 project (18). Each
environmental variable consists of a georeferenced raster layer with a spatial
resolution of 30 seconds (~1 km²) (19). Layers were
cropped using the georeferenced boundary of the state of Chiapas using R
software and its graphical interface, RStudio (47,
50).
Current
feasibility of the nine rainfed maize races
To determine
current feasibility of the nine maize races, the model was calibrated to the
current scenario using the Maxent 4.4.4 algorithm operating on presence data of
a particular species to predict its geographical distribution based on maximum
entropy (14).
The calibration
area was Chiapas, with 75% of presence records used for training and 25% for
evaluation. The model was configured with a logistic function and ten
cross-validation replicates (41).
A Jackknife test assessed variable contributions, while
predictive capacity was evaluated using the area under the curve (AUC) (39). Any replicates
under 0.9 were discarded. Then, pixel reclassifications of the resulting raster
layer of each replicate (.asc) allowed obtaining a binary map (0 = no
feasibility for the nine rainfed maize races, 1 = feasibility for the nine
rainfed maize races) using the 10th percentile presence value
as cut-off point (27).
Using QGIS version
3.285 (43), the binary map was multiplied for
each cross-validation replicate, obtaining a unique raster for each maize race.
Binary maps (rasters) were polygonised, and each polygon on the map was
dissolved to obtain surface areas in hectares and generate maps of regional
feasibility for the nine rainfed maize races.
Feasibility
of the nine rainfed maize (Z. mays) races under climate change scenarios
Feasibility of the
nine rainfed maize races in Chiapas under climate change scenarios (2041-2060
and 2081-2100) was modeled using the same presence records and environmental
variables as the current scenario. Similarly, the same parameters were used in
MaxEnt.
The HadGEM3-GC31-LL
circulation model and the 4.5 and 8.5 greenhouse gas (GHG) concentration
pathways were used (46). These scenarios
represent different projections of the future (38). The Shared
Socioeconomic Pathways (SSP) 245 scenario (GHG concentration pathway 4.5)
anticipates a middle-of-the-road pathway where trends continue their historical
patterns without significant deviations (38). The SSP 585
scenario (GHG concentration pathway 8.5) assumes low population growth and
includes rapid technological change, paired with intensive use of fossil fuels,
implying higher levels of greenhouse gas emissions (16).
Results
Feasibility
of the nine rainfed maize (Z. mays) races in Chiapas under climate
change scenarios
According to the MaxEnt model, by 2060 under the 4.5
concentration pathway (SSP 245) scenario, the suitable area for rainfed
cultivation will shrink for all nine maize races (table 1; figure 1). The Olotón race
will be most affected, with a projected cultivation area of 6,356 ha, followed
by Comiteco, with a projected area of 79,303 ha. Under the 8.5 concentration
pathway (SSP 585) scenario, all races will experience similar declines, with
Olotón reduced to 990 ha.
Table 1. Feasibility
of rainfed cultivation area for nine maize races (Zea mays) in 2060 and
2100 under two greenhouse gas concentration pathway scenarios (4.5 and 8.5) in
Chiapas, Mexico.
Tabla
1. Superficie de factibilidad de
cultivo de temporal de nueve razas de maíz (Z. mays) para los escenarios
de vía de concentración de 4.5 y 8.5 al año 2060 y 2100, en Chiapas, México.
Figure
1. Feasibility of rainfed areas for nine maize (Zea
mays) races in 2060 under two greenhouse gas concentration pathway
scenarios (4.5 and 8.5) in Chiapas, Mexico.
Figura
1. Factibilidad de la superficie de
temporal para las nueve razas de maíz estudiadas (Z. mays) para el año
2060, bajo dos escenarios de trayectorias de concentración de gases de efecto
invernadero (4.5 y 8.5), en Chiapas, México.
The variables that
contributed the most to the 2060 model under the 4.5 concentration pathway
scenario were: “maximum temperature of the warmest month” (Bio 5), explaining
74% of modeling results for the Zapalote Chico race; “mean diurnal range” (Bio
2), contributing 22.8%, 26.6%, 30.2%, and 16.4% for the Cubano Amarillo,
Tuxpeño, Vandeño, and Comiteco races, respectively; “average precipitation for
December” with 20.4% and 21.6% for the Tepecintle and Zapalote Grande races,
respectively; “precipitation seasonality” (Bio 15) contributing 22.2% for the
Olotillo race; and finally, “average minimum temperature for June”,
contributing 39.3% for the Olotón race. Under the 5.8 concentration pathways,
the most influential variables were: “maximum temperature of the warmest month”
(Bio 5), contributing 92.6% for the Zapalote Chico race; “mean diurnal range”
(Bio 2), with 27%, 34.5%, 31.3%, and 15.4% for Cubano Amarillo, Tuxpeño,
Vandeño, and Comiteco, respectively; “average precipitation for December” with
19.2% and 21.8% for Tepecintle and Zapalote Grande, respectively;
“precipitation seasonality” (Bio 15), contributing with 21.5% for the Olotillo
race; and finally, “average minimum temperature for June” contributing 40.1%
for the Olotón race.
According to the
MaxEnt model in the concentration pathway scenario 4.5 (SSP 245), similarly to
the 2060 scenario, in the 2100 scenario, all nine maize races will experience a
rainfed area reduction (table
1,
figure
2).
Figure
2. Feasibility of rainfed area for nine maize (Z.
mays) races in 2100, under two greenhouse gas concentration pathway
scenarios (4.5 and 8.5), in Chiapas, Mexico.
Figura
2. Factibilidad de la superficie de
temporal para las nueve razas de maíz estudiadas (Z. mays) para el año
2100 bajo dos escenarios de trayectorias de concentración de gases de efecto
invernadero (4.5 y 8.5), en Chiapas, México.
The Olotón race
will drastically reduce its area to 16,425 ha. In the concentration pathway
scenario 8.5 (SSP 585), the rainfed area of nine maize races will be
significantly reduced, with the Olotillo and Olotón races disappearing by the
year 2100.
The variables that most contributed to the 2100 model, in the
concentration pathway scenario 4.5, were “maximum temperature of the warmest
month” (Bio 5) with 59.3% contribution for the Zapalote Chico race, “mean
diurnal range” (Bio 2) with 22.2%, 34.7%, 34.9%, 16.9%, and 23% for the Cubano
Amarillo, Tuxpeño, Vandeño, Comiteco, and Olotillo races respectively, “average
precipitation for October” with 21.5% for the Zapalote Grande race, “average
precipitation for December” with 21.7% for the Tepecintle race, and finally,
“average minimum temperature for June” with 40.8% for the Olotón race. For the
concentration pathways 5.8 were “maximum temperature of the warmest month” (Bio
5) with 89.1% contribution for the Zapalote Chico race, “mean diurnal range”
(Bio 2) with 25.9%, 31.5%, 31.3%, 17.2%, and 19.8% for the Cubano Amarillo,
Tuxpeño, Vandeño, Comiteco, and Olotillo races respectively, “average
precipitation for October” with 23.4% for the Zapalote Grande race, “average
precipitation for December” with 13.4% for the Tepecintle race, and finally,
“average minimum temperature for June” with 36.9% for the Olotón race.
Discussion
Chiapas has the
largest maize cultivation area in all of Mexico (approximately 900,000
hectares), ranking fourth in national production, with 294,468 maize producers.
Rainfed maize accounts for 98% of this area (33). However, climate
change may reduce rainfed areas, limiting production and threatening food
security for Chiapas’ rural population (1). As a result,
communities may lose physical, social and economic access to many maize races,
reducing the availability of safe and nutritious food (19).
According to the MaxEnt model, climate change will significantly
impact feasibility of cultivation areas for the 9 maize races in Chiapas, as
suggested for other regions of Mexico (22, 30, 55) and other
countries (4, 9, 17). For example, the
feasible rainfed area for the Zapalote Chico race decreases drastically under
climate change scenarios, dropping from 4,846,495 ha
to 728,708 ha by 2060 and to 368,273 ha by 2100 with the 4.5 concentration
pathway (figure
3
and figure
4),
due to increased maximum temperatures of the warmest month (Bio 5) and
decreased precipitation of the warmest quarter (Bio 18).
Figure 3. Feasibility
of rainfed areas for the Zapalote Chico maize race (Z. mays) by 2060
under two greenhouse gas concentration pathway scenarios (4.5 and 8.5), in
Chiapas, Mexico.
Figura
3. Factibilidad de la superficie de
temporal para la raza de maíz Zapalote Chico (Z. mays) para el año 2060
bajo dos escenarios de trayectorias de concentración de gases de efecto
invernadero (4.5 y 8.5), en Chiapas, México.
Figure 4. Feasibility
of rainfed areas for the Zapalote Chico maze race (Z. mays) by 2100
under two greenhouse gas concentration pathway scenarios (4.5 and 8.5) in
Chiapas, Mexico.
Figura
4. Factibilidad de la superficie de
temporal para la raza de maíz Zapalote Chico (Z. mays) para el año 2100
bajo dos escenarios de trayectorias de concentración de gases de efecto
invernadero (4.5 y 8.5), en Chiapas, México.
Additionally, the feasible rainfed area for Olotón and Olotillo
also decreases drastically under climate change scenarios. Olotón rainfed area
decreases from 286,507 ha to 6,356 ha by 2060 and to 16,425 ha by 2100 with the
4.5 concentration pathway, while the Olotillo rainfed area decreases from
632,659 ha to 294,904 ha by 2060 and to 34,086 ha by 2100 with the 4.5
concentration pathway. But, by 2100, under the 8.5 concentration pathway, these
two maize races will disappear from Chiapas after the influence of mean diurnal
range and the increasing average minimum temperature for June, as for Zapalote
Chico.
Suitable
environmental conditions in Chiapas, derived from the MaxEnt model, under the
current scenario for rainfed Zapalote Chico race are temperatures ranging from
8.2°C to 29.1°C, accumulated precipitation from 80.3 mm to 2,092 mm, and an
elevation ranging from -2 to 2,155 m.a.s.l (3,
6, 36). Suitable environmental conditions in Chiapas, derived from the
Maxent model for the current scenario and rainfed cultivation of Olotón race,
are temperatures ranging from 4.4°C to 21.1°C, accumulated precipitation from
282.2 mm to 1,630 mm, and an elevation ranging from 384 to 2,732 m.a.s.l.,
while for Olotillo race, suitable conditions include ambient temperatures from
4.7°C to 26.1°C, accumulated precipitation from 196.1 mm to 1,695 mm, and
elevations from 112.7 meters to 2,302 m a. s. l. (3,
6, 36).
Currently, the
geographic areas of Chiapas meet these environmental conditions, allowing
optimal development of all 9 races (6, 11), preventing both
water and thermal stress (42). However, the
increase in temperature due to climate change will shorten the cultivation
period by accelerating growth rates (25). Exposure to high
temperatures causes severe damage and cellular collapse (8), leading to
pollination failure, fruit abortion and reduced load, promoting vegetative
growth (15). This ultimately results in
significant losses of aerial biomass, poor seed production, and reduced grain
yield expressed as fewer grains per cob, and therefore, lower overall yields (26,
48).
High temperatures
and less rainfall will negatively impact rainfed cultivation of these races in
Chiapas. Water is essential for plant growth (5), and a limiting
resource in rainfed cultivation. Reduced precipitation causes water stress and
plant death when transpiration exceeds cavitation thresholds (29). Transpiration is
closely associated with CO2 exchange for photosynthesis and is
essential for plant growth and development (53). On the other
hand, reduced precipitation further delays stigma exposure (pollen release),
leading to reduced crop productivity (58).
Rainfed maize cultivation is fundamental for food security in
Chiapas and Latin America, contributing to local and regional economic growth (7). Beyond its
economic importance, maize holds deep cultural significance for various ethnic
groups, playing an integral role in their worldview, ranging from traditional cuisine
to its use in sacred rituals (6). Nutritionally,
maize is closely associated with nixtamalization, a process that involves
treating it with lime. This technique enhances calcium bioavailability and
improves protein assimilation, allowing for the preparation of tortillas, an
essential and highly nutritious staple (40).
This study shows
how climate change will negatively impact nine rainfed maize races in Chiapas,
offering key insight for future sustainable management protocols and/or
conservation actions (23). However, a more
comprehensive long-term cohort study should also consider social, economic, and
cultural factors. Recognizing the importance of the social context in such
research will value both subjective experiences and the understanding of
sociocultural realities (35).
Conclusions
Our results suggest that the nine maize (Z. mays) races
currently grown in Chiapas, Mexico, will experience a dramatic decrease in
their rainfed cultivation area under climate change scenarios for the years
2060 and 2100.
1.
Ahumada, R.; Velázquez, G.; Flores, E.; Romero, J. 2014. Impactos potenciales
del cambio climático en la producción de maíz. Rev. Investigación y Ciencia.
61: 48-53.
2.
Aiello-Lammens, M. E.; Boria, R. A.; Radosavljevic, A.; Vilela, B.; Anderson,
R. P. 2015. spThin: an R package for spatial thinning
of species occurrence records for use in ecological niche models. Ecography.
38: 541-545. DOI: https:// doi.org/10.1111/ecog.01132
3.
Aragón, F.; Taba, S.; Hernández, J. M.; Figueroa, J. D.; Serrano, V. 2004.
Informe final del Proyecto CS002: Actualización de la información sobre maíces
criollos de Oaxaca. Secretaria de Agricultura, Ganadería, Desarrollo Rural,
Pesca y Alimentación. México. D. F. http://www.
conabio.gob.mx/institucion/proyectos/resultados/InfCS002.pdf (fecha de
consulta: 14/11/2023).
4.
Avilés, M. 2022. Zonificación agroecológica del cultivo de maíz (Zea mays)
y su adaptabilidad a los posibles cambios climáticos del Cantón Buena Fé,
Provincia Los Ríos. Rev. Multidiscipinar. 6(6): 6484-6501. DOI:
10.37811/cl_rcm.v6i6.3900
5.
Azcón-Bieto, J.; Talón, M. 2013. Fundamentos de Fisiología Vegetal. Barcelona.
Ed. McGRAW-HILL. 639 p.
6.
Brush, S. B.; Perales, H. 2007. A maize landscape: Ethnicity and
agro-biodiversity in Chiapas, Mexico. Rev. Agriculture, Ecosystems &
Environment. 121: 211-221. DOI: https://doi. org/10.1016/j.agee.2006.12.018
7.
Bula, A. 2020. Importancia de la agricultura en el desarrollo socio-económico.
https:// observatorio.
unr.edu.ar/wp-content/uploads/2020/08/Importancia-de-la-agricultura-en-el-desarrollo-socio-econ%C3%B3mico.pdf
(Fecha de consulta: 27/02/2024).
8.
Chaves-Barrantes, N. F.; Gutiérrez-Soto, M. V. 2017. Respuesta al estrés por
calor en los cultivos. II. Tolerancia y tratamiento agronómico. Rev. Agronomía
Mesoamericana 1: 255-271. DOI: https://doi.org/10.15517/am.v28i1.21904
9.
Chimborazo, E. 2020. Modelación de nichos ecológicos bajo dos escenarios de
cambio climático para maíz Chulpi (Zea mays) en la provincia de
Cotopaxi, en el período 2019-2020. Tesis de grado de Ingeniería en Medio
Ambiente. Facultad de Ciencias Agropecuarias y Recursos Naturales. Universidad
Técnica de Cotopaxi. Ecuador. 71 p.
10.
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
2011. Base de datos del proyecto global “Recopilación, generación,
actualización y análisis de información acerca de la diversidad genética de
maíces silvestres en México”. https://www.
biodiversidad.gob.mx/diversidad/proyectoMaices (Fecha de consulta: 20/03/2023).
11.
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
2020. Razas de maíz en México. https://www.biodiversidad.gob.mx/diversidad/alimentos/maices/
razas-de-maiz (Fecha de consulta: 18/03/2023).
12.
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
2023. Portal de información geográfica.
http://www.conabio.gob.mx/informacion/gis/ (Fecha de consulta: 06/02/2023).
13.
Comité de Información Estadística y Geografía (CEIEG). 2024. Datos Geográficos
del Estado de Chiapas. Disponible en:
https://ceieg-test.subseplan.chiapas.gob.mx/info-geografica (Fecha de consulta:
29/07/2023).
14.
Cruz-Cárdenas, G.; Villaseñor, J. L.; López-Mata, L.; Martínez-Meyer, E.;
Ortíz, E. 2014. Selección de predictores para el modelado de la distribución de
especies en MaxEnt. Rev. Chapingo serie Ciencias forestales y del ambiente. 2:
187-201. DOI: https://doi.org/10.5154/r.
rchscfa.2013.09.034
15.
Dol, G.; Huvermann, R. 2020. Interacciones entre plantas y polinizadores a
altas temperaturas.
https://www.koppert.mx/content/mexico/noticias/2020/2020_10_19__
Informaci%C3%B3n_importante_sobre_sistemas_de_polinizaci%C3%B3n_natural_en_altas_temperaturas/Interacciones_entre_plantas_y_polinizadores_a_altas_temperaturas.
pdf (fecha de consulta: 21/03/2024).
16.
Escoto, A.; Sánchez, L.; Gachuz, S. 2017. Trayectorias Socioeconómicas
Compartidas (SSP): nuevas maneras de comprender el cambio climático y social.
Rev. Estudios demográficos y urbanos. 32 (3): 669-693. DOI:
http://dx.doi.org/10.24201/edu.v32i3.1684
17.
Esperanza, M. 2013. Efectos del cambio climático en el rendimiento de tres
cultivos mediante el uso del modelo AquaCrop. Instituto de Hidrología,
Meteorología y Estudios Ambientales. Colombia.
18.
Fick, S. E.; Hijmans, R. J. 2017. WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas. International Journal of Climatology.
DOI: 10.1002/joc.5086
19.
Food and Agriculture Organization of the United Nations (FAO). 2008. Climate
change and food security: A framework document.
https://web.archive.org/web/20220325152622id_/
https://www.fao.org/3/k2595e/k2595e00.pdf (accessed July 2023).
20.
Goodman, M. M.; McK Bird, R. 1977. The races of maize IV: tentative grouping of
219 Latin American races. Rev. Econ Bot. 31: 204-221. DOI:
https://doi.org/10.1007/BF02866591
21.
Granados-Ramírez, R.; Longar, M. P. 2008. Variabilidad pluvial, agricultura y
marginación en el estado de Michoacán. Rev. Análisis económico 54: 283-303.
22.
Guajardo-Panes, R. A.; Granados-Ramírez, G. R.; Sánchez-Cohen, I.;
Barradas-Miranda, V. L.; Gómez- Rojas, J. C.; Díaz-Padilla, G. 2018.
Rendimientos de maíz (Zea mays L.) en escenarios de cambio climático en
la región de la antigua, Veracruz-México. Rev. Agrociencias. 5: 725-739.
23.
Ibarra-Montoya, J. L.; Rangel-Peraza, G.; González-Farias, F. A.; Anda, J.;
Martínez-Meyer, E.; Macias-Cuellar, H. M. 2012. Uso del modelado de nicho ecológico
como una herramienta para predecir la distribución potencial de Microystis
sp (cianobacteria) en la Presa Hidroeléctrica de Aguamilpa, Nayarit,
México. Rev. Ambi-Agua. 1: 218-234. DOI: http://
dx.doi.org/10.4136/ambi-agua.607
24.
Kato, T. A.; Mapes, C.; Mera, L.; Serratos, J.; Bye, R. A. 2009. Origen y
diversificación del maíz. Ediciones Universidad Nacional Autónoma de México y
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. 116p.
25.
Kumar, S. N.; Aggarwal, P. K.; Rani, S.; Jain, S.; Saxena, R.; Chauhan, N.
2011. Impac of climate change on crop productivity in Western Ghats, coastal
and northeastern regions of India. Current Science. 3: 332-341.
26.
Lawlor, D. W. 2005. Plant responses to climate change: Impacts and adaptation.
p. 81-88. DOI: 10.1007/4-431-31014-2_10
27.
Liu, C.; Berry, P. M.; Dawson, T. P.; Pearson, R. G. 2005. Selecting thresholds
of occurrence in the prediction of species distribution. Ecography. 28:
385-393. DOI: https://doi.org/10.1111/ j.0906-7590.2005.03957.x
28.
López-Romero, G.; Santacruz-Varela, A.; Muñoz-Orozco, A.; Castillo-González,
F.; Córdova-Téllez, L.; Vaquera-Huerta, H. 2005. Caracterización morfológica de
poblaciones nativas de maíz del Itsmo de Tehuantepec, México. Rev.
Interciencia. 5: 284-290.
29.
Luna-Flores, W.; Estrada-Medina, H.; Jiménez-Osornio, J. J.; Pinzón-López, L.
L. 2012. Efecto del estrés hídrico sobre el crecimiento y eficiencia del uso
del agua en plántulas de tres especies arbóreas caducifolias. Rev. Terra
latinoamericana. 4: 343-353.
30.
Magaña, A. L. 2014. Evaluación integral de los impactos de la variabilidad y el
cambio climático en la agricultura de maíz en el estado de Michoacán. Tesis de
grado en maestría en administración integral del ambiente. EL Colegio de la
Frontera Norte. Tijuana. México. 157 p.
31.
Martínez, E. 2015. Maíz, milpa, milperos y agricultura campesina en Chiapas.
Resumen de tesis de grado en doctor en Desarrollo Rural. División de Ciencias
Sociales y Humanidades. Universidad Autónoma Metropolitana. Xochimilco. 81 p.
32.
Massieu, Y.; Lechuga, J. 2002. EL maíz en México: biodiversidad y cambios en el
consumo. Rev. Análisis económico. 36: 281-303.
33.
Mercer, K. L.; Perales, H. 2019. Estructura de la adaptación local a lo largo
del paisaje: tiempo de floración y aptitud en variedades locales de maíz
mexicano (Zea mays L. subsp. mays). Rev. Genetic Resources and Crop
Evolution. 66: 27-45. DOI: https://doi.org/10.1007/s10722- 018-0693-7
34.
Nelson, G. C.; Rosegrant, M. W.; Koo, J.; Robertson, R.; Sulser, T.; Zhu, T.;
Ringler, C.; Msangi, S.; Palazzo, A.; Batka, M.; Magalhaes, M.;
Valmonte-Santos, R.; Ewing, M.; Lee, D. 2009. Cambio climático: El impacto en
la agricultura y los costos de adaptación. Washington, D. C. Ediciones
Instituto Internacional de Investigación sobre políticas alimentarias. 23 p.
DOI: 10.2499/0896295370
35.
Noriega, B. S.; Rodríguez, R. E.; López, I. A.; Buchi, C. S.; Girón, M. H.;
Flores, M. A. 2021. Importancia del contexto social para la investigación.
Revista científica del Sistema de Estudios de Posgrado. 1: 77-87. DOI:
https://doi.org/10.36958/sep.v4i1.77
36.
Nuricumbo, A. 2015. Zapalote Chico: Soberanía alimentaria en el Itsmo de
Tehuantepec, México. Facultad de Ciencias. Universidad de Vigo. Tesis de grado
doctoral en Ciencia y Tecnología Agroalimentaria. España. 83 p.
37.
Olivo, M. L.; Soto-Olivo, A. 2010. Comportamiento de los gases de efecto
invernadero y las temperaturas atmosféricas con sus escenarios de incremento
potencial. Revista de la Universidad Experimental Politécnica. 57: 221-230.
38.
O’Neill, B. C.; Tebaldi, C.; Vuuren, D. P.; Eyring, V.; Friedlingstein, P.;
Hurtt, G.; Knutti R.; Kriegler, E.; Lamarque, J. F.; Lowe, J.; Meehl, G. A.;
Moss, R.; Riahi, K.; Sanderson, B. M. 2016. The Scenario Model Intercomparison
Project (ScenarioMIP) for CMIP6. Geoscientific Model Development. 9: 3461-3482.
DOI: 10.5194/gmd-9-3461-2016
39.
Palma-Ordaz, S.; Delgadillo-Rodríguez, J. 2014. Distribución de ocho especies
exóticas de carácter invasor en el estado de Baja California, México. Botanical
Sciences. 92(4): 587-597.
40.
Perales, H. 2012. Maíz: nuestra herencia y responsabilidad. Ecofronteras. 46:
2-5.
41.
Phillips, S. J.; Anderson, R. P.; Schapire, R. E. 2006. Maximum entropy
modeling of species geographic distributions. Ecological Modelling. 190:
231-259. DOI: https://doi.org/10.1016/j.
ecolmodel.2005.03.026
42.
Pozo-Gómez, D. M.; Orantes-García, C.; Sánchez-Cortéz, M. S.; Rioja-Paradela,
T.; Carrillo-Reyes, A. 2022. Potential distribution of Croton guatemalensis:
A model with reproductive biology data. Botanical Sciences. (100)2: 291-299.
DOI: https://doi.org/10.17129/botsci.2865
43.
QGIS.org. 2023. QGIS Geographic Information System. QGIS Association.
http://www. qgis.org (Accessed June 2023).
44.
Quevedo, D. C.; Cervantes, J.; Noriero, L.; Zepeda, J. M. 2017. Maíz: Sustento
de vida en la cultura Teenek. Comunidad Tamaletom, Tancanhuitz, S.L.P, México.
Revista de Geografía Agrícola 58: 5-19.
45.
Ramírez, O.; Ibarra, D. G.; Gutiérrez, A. 2023. Análisis económico de la
producción de maíz en Chiapas, México, en la región Frailesca. Revista
Científica Multidisciplinar. 4: 423-437. DOI:
https://doi.org/10.37811/cl_rcm.v7i4.6879
46.
Ramírez-Magil, G.; Botello, F.; Navarro-Martínez, A.; Ramírez-Magil, G.;
Botello, F.; Navarro-Martínez, A. 2020. Idoneidad de hábitat para Swietenia
macrophylla en escenarios de cambio climático en México. Rev. Madera y
bosques. 3. DOI: https://doi.org/10.21829/ myb.2020.2631954
47.
R Core Team. 2022. R: A Language and Environment for Statistical Computing. R
Foundation for Statistical Computing. https://www.R-project.org/ (Accessed May
2023).
48.
Rincón-Tuexi, J. A.; Castro-Nava, S.; López-Santillán, J. A.; Huerta, A. J.;
Trejo-López, C.; Briones-Encinia, F. 2006. Temperatura alta y estrés hídrico
durante la floración en poblaciones de maíz tropical. Revista Internacional
Botánica Experimental. 75: 31-40.
49.
Rivera, D. A. 2020. Impacto del cambio climático en la distribución potencial
de especies nativas de interés agrícola en México. Instituto de Ciencias.
Benemérita Universidad Autónoma de Puebla. Tesis de grado en maestría en Manejo
Sostenible de Agroecosistemas. Puebla. 62 p.
50.
RStudio Team. 2022. RStudio: Integrated Development Environment for R. RStudio.
PBC. Boston.
51.
Ruiz, J, A.; Durán, N.; Sánchez, J. J.; Ron, J.; González, D. R.; Holland, J.
B.; Medina, G. Climatic rdaptation and ecological descriptors of 42 Mexican
maize races. Crop Science. 48: 1502-1512. DOI:
https://doi.org/10.2135/cropsci2007.09.0518
52.
Sánchez, J, J.; Goodman, M. M.; Stuber, C.W. 2000. Isozymatic and morphological
diversity in the races of maize of Mexico. Economic Botany. 54: 43-59. DOI:
https://doi.org/10.1007/ BF02866599.
53.
Santiago, A. 2010. Aspectos básicos de la fisiología en respuesta a estrés y el
clima como condicionante del mismo en las plantas.
https://exa.unne.edu.ar/biologia/ fisiologia. vegetal/Aspectosb%C3%A1sicosfisiolog%C3%ADarespuestaestr%C3%A9s.pdf
(fecha de consulta: 12/11/2023).
54.
Sistema Nacional de Información sobre Biodiversidad (SNIB). 2023. Precipitación
anual en México (1910-2009).
http://www.conabio.gob.mx/informacion/gis/?vns=gis_root/clima/
precip/preanu13gw (fecha de consulta: 25/02/2023).
55.
Soto, L.; Jiménez, G.; Lerner, T. 2008. Diseño de sistemas agroforestales para
la producción y la conservación. Ediciones El Colegio de la Frontera Sur. 90 p.
56.
Tinoco-Ruedas, J.; Gómez-Díaz, J.; Monterroso-Rivas, A. 2011. Efectos del cambio
climático en la distribución potencial del maíz en el estado de Jalisco,
México. Rev. Terra Latinoamericana. 29(2): 161-168.
57.
Ureta, C.; Martínez-Meyer, E.; Perales, H. R.; Álvarez-Buylla E. 2011.
Projecting the effects of climate change on the distribution of maize races and
their wild relatives in Mexico. Mexico. Global Change Biology. DOI:
https://doi.org/10.1111/j.1365-2486.2011.02607.x
58.
Zarco, E.; González, V. A.; López, M. C.; Salinas Y. 2005. Marcadores
fisiológicos de la tolerancia a sequía en maíz (Zea mays L.). Rev.
Agrociencia. 5: 517-528.
Supplemmentary material