Projection of the Irrigation Water Requirement of Forage Corn Under Climate-Change Conditions in the North of Mexico

Abstract

Climate change has put the agricultural industry under enormous pressure, as rising temperatures and changing rainfall patterns are affecting crop yields and productivity. The temporal variability of the irrigation water requirement (IWR) as a function of crop evapotranspiration (ETc) and effective rainfall (Peff) was analyzed for forage corn cultivation from a climate-change perspective in the “Comarca Lagunera” region, located in the north of Mexico. The time periods 1975–2016 and 2061–2080 were analyzed, the latter using the forcings of the climate-change scenario SSP5-8.5, from the meteorological data. The Peff, ETc, and IWR for the maize crop were modeled with CROPWAT software, and the Rodionov test was applied to detect points of change in the three variables mentioned above. The historical values of IWR, ETc, and Peff values for spring were estimated at 511, 571, and 57, while for summer, they were 336, 450, and 122, respectively. The climate-change scenario toward the distant horizon projects increases in IWR of 11.9% and 3.5% and in ETc of 7.7% and 0.6%, respectively, for both spring and summer agricultural cycles, as well as decreases in Peff of −30% and −12%, respectively. These results emphasize the combined impact of rising temperatures and reduced rainfall on crop water needs, a crucial factor for crop production in regions that depend on agricultural irrigation. This study provides a foundation for planning irrigation water management in anticipation of an imminent increase in demand due to erratic weather patterns in arid zones.

1. Introduction

Irrigated agriculture is responsible for using almost 70% of the planet’s available water resources [1]. Global projections are uncertain, and food demand is expected to increase by up to 60% by 2050. This increase requires greater planting in fertile soils and more intensive production with greater use of water resources [2]. However, agricultural water scarcity is under increasing pressure due to the rapid growth in water demand for different consumptive uses in addition to the impact of climate change on the hydrological cycle [3]. Climate change is directly affecting crop evapotranspiration, leading to increased irrigation water demand, with a projected global average increase of 7–21% expected by the end of this century [4].

In this situation, one alternative solution is to estimate crop water demand. These calculations are useful, based on available regional water resources, for improving the efficiency of irrigation systems [5]. Crops require a certain amount of water for their growth and development; however, if water is not distributed according to need, losses occur, and as a result, irrigation would be applied to a smaller area with a sufficient amount of water [6]. Crop water requirements depend on climatic conditions, cultivated areas, crop species, soil types, growing seasons, and crop production frequencies [7].

In Mexico, agriculture is the most water-intensive activity, accounting for 77% of the total consumption; the remaining extraction is used for public supply (14%), thermoelectric power (5%), and self-supplied industry (4%) [8]. As an agricultural country, Mexico relies heavily on irrigation due to insufficient rainfall in agricultural areas of Central and Northern Mexico, providing water to 6.4 million hectares [9]. Maize cultivation consumes an average of 400 to 500 mm of water; however, water use is limited in both quantity and timing [10].

Maize is the most important crop in Mexico in terms of production volume, as it is used for both human consumption and animal feed [11]. Forage maize is offered as crop residue or silage to ruminant livestock, providing significant amounts of fiber and energy [12]. Irrigation District 017 is located in the Comarca Lagunera region of Northern Mexico. An average of 54,300 hectares are dedicated annually to forage maize production, making it the third most important region in Mexico for this crop. During the period 2016–2020, annual forage production reached 2.3 million tons, registering an average of 43 tons per hectare [13]. The dairy region of the “Comarca Lagunera” contributes approximately 22% of the country’s total cow’s milk production. In this sense, the increased need for products made from corn and the decrease in water availability require an optimization of resources to meet present and future needs [14].

The environmental conditions of this arid region of Mexico, allow for the production of forage maize during two agricultural cycles: spring and summer. This is achieved using a significant amount of freshwater for irrigation (2530 Mm³), which comes from the Nazas River Basin (1278 Mm³) and deep wells in different aquifers in the Comarca Lagunera region (1252 Mm³) [15]. Maize production during these two agricultural cycles satisfies the forage needs of dairy cattle in this region [12,16].

Climate change, coupled with increased water demand from industry, is generating a greater need for irrigation systems [17]. When irrigation water is required, evapotranspiration and rainfall provide an indicator of the amount of water a crop consumes during its growth cycle, influencing decisions about the necessary irrigation amount [18]. In developing countries, farmers apply more water than necessary to their crops due to a lack of accurate information about crop water requirements and the belief that more abundant irrigation translates to better harvests [19]. The CROPWAT tool is being researched as a resource for measuring plant water use, calculating the amount of irrigation and total water consumed based on crop type in a specific area, and evaluating the variations in water consumption and irrigation caused by climate change [18,20].

Therefore, the objectives of this research were to determine the temporal variability of the irrigation water requirement (IWR) from crop evapotranspiration (ETc) and effective precipitation (Peff) for the cultivation of forage maize in an arid climate of Northern Mexico, such as the “Comarca Lagunera”, and to identify the main threats from a climate-change perspective, which, globally, causes erratic and more difficult environmental conditions for the establishment and development of crops in arid areas. Water resources in the region are becoming increasingly scarce due to population and industrial growth. This puts pressure on both underground and surface water sources, which threatens to deprive agriculture of water.

2. Materials and Methods

2.1. Study Area

This research was carried out in two municipalities in the state of Durango, Mexico: Gómez Palacio (25°36′02.39″ N; 103°29′36.20″ W; 1132 m) and Lerdo (25°28′51.03″ N; 103°39′54.84″ W; 1145 m). These municipalities are located in the central part of the Comarca Lagunera region and are the main producers of forage maize. In 2023, the cultivated areas for these two municipalities were reported to be 18,854 and 7830 hectares, respectively [13]. The study area is located in Hydrological Region No. 36, in irrigation district 017 (Figure 1) [21].

According to the climatological normals of the National Meteorological Service (SMN), during the period from 1990 to 2020, the region had an average precipitation of 250 mm·year⁻¹; the average maximum temperature was 28.9 °C, with the months of May, June, and July being the hottest; while the average annual minimum temperature was 14.9 °C, with the months of December and January having the lowest temperatures, at 0 °C (Figure 2) [22].

2.2. Climatic Data

The monthly average of the maximum and minimum temperature values and precipitation recorded at six stations in the study area was used (Figure 1): 05026—PRESA COYOTE (25°32′31.89″ N; 103°28′09.04″ W), 05027—EL CUIJE (25°41′47.91″ N; 103°20′25.08″ W; 1120), 05180—FRANCISCO I. MADERO (25°46′26.95″ N; 103°15′43.06″ W), 10108—CIUDAD LERDO (25°32′45.77″ N; 103°31′19.06″ W), 10169—C.B.T.A. 101 GOMEZ PALACIO (25°48′50.15″ N; 103°34′26.84″ W), and 10170—C.B.T.A. 047 LERDO (25°30′19.98″ N; 103°39′13.19″ W), belonging to the SMN with a daily record of 42 years (1975–2016) [22]. To control the quality of the meteorological data, a process of homogenization and filling in missing data for each station was performed, using the R-CLIMATOL 3.1.1 package https://www.climatol.eu/ (accessed on 15 June 2025), following the methodology described by Guijarro [23].

Climatol uses the Standard Normal Homogeneity Test (SNHT) to check the homogeneity of climate series and the Paulhus and Kohler method to complete daily series using averages from nearby stations [24,25]. This process involves detecting outliers resulting from incorrect data entry or measurement errors and completing the missing data [26], using provisional averages and standard deviations. The process is then repeated with the complete series, and the original missing data are recalculated using the new parameters, resulting in new means and standard deviations. This process is repeated until none of the means changes when the data are rounded to their initial precision [27,28].

2.3. Climate-Change Scenarios

The Long Ashton Research Station Meteorological Generator Ver. 8.0 (LARS-WG) was used as a downscaling model for the precipitation and maximum and minimum temperatures. This program performs well under a wide range of meteorological conditions and has been calibrated and validated in various climate studies in Mexico [29]. LARS-WG generates high-resolution climate-change scenarios at a regional level [30,31].

Climate-change projections were made using CMIP6 MPI-ESM1-2-LR GCM, considering Shared Socioeconomic Pathway SSP5-8.5 (High Forcing), for a distant time horizon of 2061–2080, using daily average data from the six meteorological stations in the study area [32,33]. Xiang et al. [34] demonstrated that during an SSP5-8.5 scenario toward the distant horizon, the greatest variabilities occur in the temperature and precipitation due to climate change, altering the water requirement and evapotranspiration of a crop.

MPI-ESM1.2-LR (Max Planck Institute Earth System Model version 1.2 Low-Resolution) is a global climate model from the Max Planck Institute for Meteorology in Germany [35]. It is a state-of-the-art climate model used for climate simulations and predictions, particularly within the framework of CMIP6 (the Coupled Model Intercomparison Project). This model improves the representations of atmospheric and oceanic dynamics, allowing for reduced bias and detailed studies of climate impacts, as well as seasonal and decadal predictions, and is widely used throughout the world [36,37].

The calibration, validation, and projection of climate-change scenarios were the three processes necessary for the configuration of LARS-WG in the Comarca Lagunera, as indicated by Semenov et al. [30] (2024). This process was validated using the Kolmogorov–Smirnov (K-S) statistical test, with the purpose of verifying that the distributions of the daily climatic variables, obtained from both observed and simulated data, were similar. A p-value was also used to determine whether to accept or reject the hypothesis that both groups of data could originate from a single distribution [29].

2.4. Reference Evapotranspiration

Ref-ET software version 4.1 (Reference Evapotranspiration Calculator programming) [38] was used. Its calculations are based on meteorological data measurements provided by the user. For this research, the Hargreaves–Samani method was used, following the methodological process recommended by Allen [39]. The HS method requires only the maximum and minimum air temperatures and the solar radiation at the top of the atmosphere [40], as indicated in Equation (1):

$${\mathrm{ET}}_0 = 0.0023 ({\mathrm{T}}_{\max }-{\mathrm{T}}_{\min }{)}^{0.5}\left({\mathrm{T}}_{\mathrm{med} }+ 17.8\right) {\mathrm{R}}_{\mathrm{a}}$$

(1)

where ET₀ = reference evapotranspiration (mmd⁻¹); T_max = maximum temperature (°C); T_min = minimum temperature (°C); T_med = average temperature (°C); R_a = extraterrestrial solar radiation (in mm·day⁻¹).

2.5. CROPWAT

CROPWAT 8.0 is a decision-support software developed by the Land and Water Development Division of the Food and Agriculture Organization of the United Nations (FAO). It uses a series of equations to estimate crop evapotranspiration (ETc), effective rainfall (Peff), irrigation water requirement (IWR), and irrigation scheduling [20]. As input data, it requires crop, soil, precipitation, and climate information, which supports the calculation of the water supply for different crops under irrigated and rainfed conditions [7].

To accurately estimate the requirements of the forage corn crop in the study area, the parameters describing ETc, Peff, and IWR were calculated based on the methodologies proposed by the FAO [41].

2.5.1. Climate Data

Monthly values of reference evapotranspiration (ET₀) and precipitation (P) were used as input data for the CROPWAT model, which were obtained from the base period (1975–2016) and distant future (2060–2080) projected in the SSP5-8.5 scenario.

2.5.2. Crop Data

Crop parameters (

Table 1. Physiological and phenological parameters of the forage maize crop in the Comarca Lagunera.

Parameter	Value
Duration of the initial stage (days)	25
Duration of the development stage (days)	40
Length of mid-season (days)	30
Late season duration (days)	15
Crop coefficient (kc), early stage	0.3
Crop coefficient (kc), mid-season	1.2
Crop coefficient (kc), late season	0.35
Rooting depth, initial stage (m)	0.3
Rooting depth, mid-season (m)	1
Crop height, mid-season (m)	2.7
Critical exhaustion fraction, initial stage	0.55
Critical exhaustion fraction, mid-season stage	0.55
Critical exhaustion fraction, end of season	0.8
Performance response factor, early stage and development	0.8
Performance response factor, mid-season and late season	1.8

) were determined from the sowing, development, and harvest dates of some forage varieties, as well as the agronomic management reported in the Comarca Lagunera by Granados-Niño et al. [12] and Montemayor-Trejo et al. [15]. The sowing dates for the spring and summer cycles were fixed: March 10 and June 5, respectively.

2.5.3. Crop Evapotranspiration (ET_c)

The ET_C of the forage corn crop was calculated from Equation (2):

$${\mathrm{ET}}_{\mathrm{c}} = {\mathrm{K}}_{\mathrm{c}} \times {\mathrm{ET}}_0$$

(2)

where ET_c = crop evapotranspiration (in mm dia⁻¹); K_c = crop coefficient (dimensionless); ET₀ = evapotranspiration of the reference crop (in mm dia⁻¹).

2.5.4. Effective Rainfall (Peff)

The USDA Soil Conservation Service method was used to calculate the effective rainfall (Peff) on a monthly [42,43] basis, using the following criteria:

1. When rainfall (P) is <250 mm·year⁻¹, the effective rainfall (Peff) is obtained using Equation (3):

$$\mathrm{Peff} = (125-0.2 \times \mathrm{P}) \times \mathrm{P}/125$$

(3)

2. When rainfall (P) is >250 mm·year⁻¹, the effective rainfall (Peff) is obtained using Equation (4):

$$\mathrm{Peff} = 125 + 0.1 \times \mathrm{P}$$

(4)

where Peff = effective rainfall (mm·year⁻¹); P = rainfall (mm·year⁻¹).

2.5.5. Irrigation Water Requirement (IWR)

From the ETc calculated for the corn crop and the effective rainfall values (Peff), the irrigation water requirement (IWR) was calculated using Equation (5):

$$\mathrm{IWR} = \mathrm{ETc} – \mathrm{Peff}$$

(5)

where IWR = irrigation water requirement (mm·year⁻¹); ETc = crop evapotranspiration (mm·year⁻¹); Peff = effective rainfall (mm·year⁻¹).

2.6. Detection of Changes in IWR, ETc, and Peff

The detection of abrupt changes (turning points or breaking points) indicates the possibility of a regime shift in a time series [44]. This research used the method proposed by Rodionov [45], known as STARS (Sequential T-test Analysis of Regime Shifts), which has been applied in numerous research projects, primarily in climatology and marine ecosystem research, oceanography, hydrology, and biochemistry [46]. For this research, the free Excel add-in “SHIFT Detection” V.2.1 https://www.beringclimate.noaa.gov/regimes/ (accessded on 10 May 2025) was used. Change detection (in the mean) was performed for the IWR, ETc, and Peff time series, with a significance level of p = 0.05, as determined using Student’s t-test, a cutoff length of 10, and a Huber parameter of 1.

3. Results

3.1. Climate-Change Scenarios

The maximum and minimum temperatures and precipitation recorded in the Comarca Lagunera region during the period 1975–2016 were used to calibrate and validate LARS-WG. Subsequently, the effectiveness of this model was analyzed to perform the downscaling of the GCMs, using the Kolmogorov–Smirnov (K-S) statistical test. The results obtained show that the daily distribution of the analyzed variables—minimum and minimum temperatures, as well as precipitation—is perfect in the four seasons of the year, in both dry and wet periods (

Table 2. K-S tests and p-values for Comarca Lagunera station and the seasonal distributions of wet/dry series.

Season	Period	K-S	p Value	Assessment
DJF	wet	0.05	1	Perfect
DJF	dry	0.057	1	Perfect
MAM	wet	0.028	1	Perfect
MAM	dry	0.078	1	Perfect
JJA	wet	0.088	1	Perfect
JJA	dry	0.081	1	Perfect
SON	wet	0.075	1	Perfect
SON	dry	0.062	1	Perfect

DJF—winter, MAM—spring, JJA—summer, and SON—autumn.

), which allows us greater confinement and reliability to generate the future climate scenario.

The average annual minimum and maximum temperatures reported in the study area (Comarca Lagunera) are 14.9 °C and 28.9 °C, respectively. Based on the MCG MPI-ESM1-2-LR projection with the SSP5-8.5 trajectory for the 2061–2080 horizon, the average annual minimum temperature is projected to increase by 21% (17.1 °C), while the maximum temperature is expected to increase by up to 10.9% (31.8 °C); both variables are compared to those over the historical period (1975–2016). Figure 3 shows the percentage changes for the temperature variables, indicating an increase in all months, mainly during the winter season, with the minimum temperature showing the greatest impact. The average annual precipitation recorded for irrigation district 017 in the Comarca Lagunera is 250 mm·year⁻¹, where the MCC projects a decrease of up to 16% from the annual average, expecting an average annual precipitation of up to 210 mm·year⁻¹ in the distant future. Based on the above, SSP5-8.5 projects a warmer and drier climate in the Comarca Lagunera region compared to the historical arid climate.

3.2. Detection of Points of Change in IWR, ETc, and Peff

Using the Rodionov test applied during the spring growing season for forage maize (Figure 4), a point of change was identified for ETc in 1983; subsequently, a period of continuous stability occurred until 2014, when another significant change took place. Both changes were negative. For Peff, the same test identified only one turning point; after 40 years of stability, a positive change occurred in 2014, representing an increase in this variable. Similarly, for IWR, two turning points were detected on the timeline with respect to the mean: The first is a negative turning point in 1981, and later, toward the end of the series, another negative turning point occurred in 2014.

For the summer agricultural cycle, two abrupt changes were observed in the ETc variable, both negative relative to the mean. The first change was detected at around 1984 and the second in 2013. Regarding Peff, two points of change were also observed: the first in 1985 and the second in 2015, as shown in Figure 5. Both points of change were negative, indicating a decrease in precipitation. Finally, for the IWR variable, only one point of change was observed in 1985. These results suggest significant changes in the correlations of the climatic variables, highlighting abrupt periods of change in the analyzed data, which provide crucial information for understanding, and potentially predicting, variations in historical climate and associated agroclimatic variables.

3.3. Temporal Variations of IWR, ETc, and Peff

The results show annual variations for the three variables studied in both agricultural cycles, revealing a significant difference between them. Depending on the season, different climatic conditions are observed, such as precipitation, which is higher during the summer agricultural cycle, while the average temperature is lower during the spring cycle.

For the spring agricultural cycle, the highest IWR value occurred in 1975 at 631 mm·year⁻¹, with a recorded ETc of 632 mm·year⁻¹ and a near-zero Peff of 0.6 mm·year⁻¹. In contrast, the lowest IWR was recorded in 2016 at 380 mm year⁻¹, with an ETc of 553 mm·year⁻¹ and a Peff of 183 mm·year⁻¹. The average annual IWR is 511 mm·year⁻¹, the average ETc is 571 mm·year⁻¹, and the average Peff is 57 mm·year⁻¹.

Regarding the results obtained for the summer agricultural cycle, Figure 6 shows that the highest IWR value occurred in 1982 at 458 mm·year⁻¹; consequently, ETc also reached its highest value at 521.7 mm·year⁻¹. However, the lowest IWR was recorded in 1990 at 228 mm·year⁻¹, with an ETc of 442 mm year⁻¹. The average annual IWR for this cycle was 336 mm·year⁻¹, with ETc ranging around 450 mm·year⁻¹ and an average Peff of 123 mm·year⁻¹.

3.4. Temporal Variations of IWR, ETc, and Peff with Climate-Change Projections

MCG MPI-ESM1-2-LR with the SSP5-8.5 trajectory projects increases in IWR in the spring and summer cycles of 11.9% and 3.5%, respectively. ETc indicates increases of 7.7% for the spring cycle and 0.6% for the summer cycle; while the scenario for 2061–2080 projects significant decreases in Peff of −30% in the spring cycle and −12% in the summer cycle (

Table 3. Percentage changes in IWR, ETc, and Peff over the annual historical period (1975–2016) for the horizon 2061–2080 with SSP5-8.5.

Variable—Agricultural Cycle	1985–2016 (mm·year−1)	2061–2080 (mm·year−1)	Change (%)
IWR—spring	511.9	572.9	11.9
IWR—summer	336.6	348.2	3.5
ETc—spring	571.3	615.1	7.7
ETc—summer	450.7	453.3	0.6
Peff—spring	57.0	39.9	−30.1
Peff—summer	122.9	107.9	−12.2

).

4. Discussion

4.1. Climate-Change Scenarios

In this research, the long-term horizon (2061–2080) shows increases in the maximum temperature of 2.9 °C and the minimum temperature of 2.2 °C, consequently increasing the average annual temperature by 2.6 °C. These values indicate the variability caused by climate change in the Comarca Lagunera region and align with global temperature rise projections by 2100 (from 3.2 °C to 5.4 °C) [47,48]. In Mexico, CMIP6 scenarios with SSP5-8.5 project an increase of up to 6 °C by the end of this century in Northern Mexico [32]. In this regard, Mendoza-Hernández et al. [49] have reported increases in maximum and minimum temperatures of up to 4 °C in Comarca Lagunera. Furthermore, RCP-8.5 projects a decrease in precipitation relative to the average for the period 2021–2050, leading to a more desert-like environment. Nationally, precipitation is expected to decrease by an average of 15.2%, while for the Comarca Lagunera region, the scenario projects an estimated decrease of 12.9%. This condition will result in reduced runoff in basins located in arid zones, meaning a decrease in the available surface water [50].

4.2. Detection of Point-of-Change Temporal Variations in IWR, ETc, and Peff

The Rodionov test is based on a sequential t-test analysis, where a regime-change index is calculated to accept or reject the hypothesis of a regime change in each observation, determining whether subsequent values are significantly different from the mean of the previous regime [46]. For example, two change points could be identified in the ETc variable. Only one change point is present for Peff and two for IWR (Figure 5). These points indicate significant changes in all three variables [51].

In this study, Rodionov’s sequential approach, based on the weighted means of the IWR, ETc, and Peff regimes, using Huber’s weighting function with parameter α = 1, revealed abrupt changes in the analyzed variables, implying notable climate regime shifts [46]. For the spring cycle, the average Peff value changed in 1984, increasing by 21.2 mm·year⁻¹ until 2014, when it decreased by 54.2 mm·year⁻¹. Due to these increases, the average IWR decreased by 58 mm·year⁻¹ in the same year (1984); meanwhile, the average ETc variable shows a decrease of 37.4 mm·year⁻¹ toward 1983, while for the summer cycle, Peff showed an increase of 94.9 mm·year⁻¹ until 2013 with respect to that in the historical period. In the same year, it was identified that IWR also decreased by 114 mm·year⁻¹, while ETc showed a decrease of −45 mm·year⁻¹ in 1983; and in 2014, it decreased again.

Guzmán-Luna et al. [52] identified, using Rodionov’s method, an increase in the average irrigation requirements for the spring growing season in Central Mexico. This increase was evident starting in 1995 with a cutting length of 20, while with a cutting length of 10, an increase was observed since 2016. Both increases were reflected in the rise in the average temperature during the agricultural cycle.

4.3. Temporal Variations of IWR, ETc, and Peff in Response to Climate Change

In the Comarca Lagunera region, annual variations are observed for the three variables studied (IWR, ETc, and Peff) in both agricultural cycles, showing a significant difference between them. Depending on the season, different climatic conditions are present in the study area, such as annual precipitation, which is higher during the summer agricultural cycle and lower in spring, with accumulations of 67% and 12%, respectively; while the average temperature is lower during the spring agricultural cycle (23.7 °C) compared to that during the summer agricultural cycle (26 °C) [15].

The temporal variation of the IWR in the Comarca Lagunera region is closely linked to the variation and changes in Peff and ETc for forage maize crops [53]. This condition is exacerbated by drought years, which are very common in arid zones of Northern Mexico, as reported by Velázquez-Zapata and Dávila-Ortiz [54]. They indicate that during 2011 and 2012, more than 85% of the country was affected by severe to extreme drought, causing water scarcity and heat waves that resulted in the loss of six million hectares of crops. Furthermore, droughts cause increased pressure on groundwater resources, which are the only source of supply for agricultural irrigation in these crops [55].

For the spring agricultural cycle, due to the lack of rain, it is necessary to apply up to five layers of irrigation in the different phenological stages of forage maize [16]. During the summer agricultural cycle, with greater precipitation, irrigations applied to the crop are reduced to one from two, allowing for storage in dams and reservoirs, as well as the recharge of aquifers in the Comarca Lagunera, which are overexploited [15,16,17,18,19,20,21].

Şen [1] identified a significant increase in the average temperature in the Cukurova region, particularly during summers, using the MCG HadGEM2-ES model with the RCP8.5 scenario and a significant (20%) increase in ETc in maize in a distant future scenario; while the irrigation water requirement increased from 459 mm·year⁻¹ to 618 mm year⁻¹ during the historical period (1971–2000) in the future projection (2069–2098). Shiferaw et al. [56] reported for the Ethiopian River Basin that during the historical period (1992–2015), the averages for Peff, ETc, and PP were 148 mm·year⁻¹, 454.7 mm·year⁻¹, and 306.1 mm·year⁻¹, respectively, using CanRCM4 GCM with the RCP8.5 scenario, where they identified a projected mean Peff of 164 mm·year⁻¹, an ETc of 482 mm·year⁻¹, and an IWR of 330.3 mm·year⁻¹, increases in the three variables of 10%, 6%, and 7%, respectively. Meanwhile, Saeed et al. [17] identified potential increases in ET₀ and IWR in the semi-arid continental region of the Middle East north of Iraq, with a long-term horizon, reporting that ET₀ increased by 15.9%, while IWR rose by 8.8%, with a 19.8% increase in the average temperature compared to the historical average of 39 °C.

Figure 7 presents the temporal behaviors of the variables IWR, ETc, and Peff in the SSP5-8.5 scenario for the period 2061–2080. It can be observed that for both agricultural cycles, IWR increases toward the end of the time series. This behavior is due to the increase in ETc since as the average temperature increases, so does ETo; consequently, precipitation decreases in both periods [56]. As a result of climate change, IWR will be higher in many corn-producing regions for grain and forage, which will cause demand problems for these products [6].

It has been reported that increases in maximum and minimum temperatures (Tmax and Tmin), caused by climate change, result in a shorter maize-growing season. This leads to a faster accumulation of growing degree days (GDD), which, in turn, reduces the number of days needed to reach a given phenological stage. This situation will demand a greater amount of rainfall to meet the water requirements of the maize [57,58].

5. Conclusions

The SSP5-8.5 scenario of MCG MPI-ESM1-2-LR, applied to the Comarca Lagunera region for the distant future (2061–2080), projects an increase in average temperature of 3.1°C and a decrease in precipitation of 41 mm/year, which directly impact variables associated with forage maize production, such as crop evapotranspiration, effective precipitation, and, consequently, irrigation water requirements. Crop evapotranspiration (ETc) for forage maize in the Comarca Lagunera region is higher than effective precipitation (Peff) in both growing seasons. Therefore, a significant amount of water must be provided to the crop through an irrigation system. Projected temperature increases in the SSP5-8.5 climate-change scenario will result in higher ETc and, consequently, a greater irrigation water requirement. This would jeopardize the production of forage maize in the distant future, significantly reducing the area planted with this crop. In the region, water resources are under pressure from urban, industrial, and livestock uses.

This study highlights the importance of improving irrigation efficiency in the Comarca Lagunera region to maintain a sustainable supply of irrigation water in the face of climate change, as water availability for agriculture could be a major challenge in the future. Furthermore, it is necessary to implement mitigation and adaptation measures to reduce water use, such as water storage, the use of drought- and high-temperature-tolerant forage maize seeds, adjusting planting dates, soil conservation, the use of biofertilizers, and improved water management, among others. All these measures could prove to be useful in combating the challenge of water availability for agriculture in the future.

Finally, it should be mentioned that a limitation in this research was the lack of historical irrigation records to validate the Cropwat model; however, with the information simulated by the program, it was possible to evaluate the irrigation water requirement, obtaining reliable results.