Agroclimatic Indicator Analysis Under Climate Change Conditions to Predict the Climatic Suitability for Wheat Production in the Upper Blue Nile Basin, Ethiopia

Abstract

Agricultural productivity is significantly influenced by climate-related factors. Understanding the impacts of climate change on agroclimatic conditions is critical for ensuring sustainable agricultural practices. This study investigates how key agroclimatic variables—temperature, moisture conditions, and length of the growing season (LGS)—influence wheat suitability in the Upper Blue Nile Basin (UBNB), Ethiopia. The Global Agroecological Zones (GAEZ) methodology was employed to assess agroclimatic suitability, integrating climate projections from Climate Models Intercomparison Project v6 (CMIP6) under shared socioeconomic pathway (ssp370 and ssp585) scenarios. The CMIP6 data provided downscaled projections for temperature and precipitation, while the GAEZ framework translated these climatic inputs into agroclimatic indicators, enabling spatially explicit analyses of land suitability. Projections indicate significant warming, with mean annual temperatures expected to rise between 1.13 °C and 4.85 °C by the end of the century. Precipitation levels are anticipated to increase overall, although spatial variability may challenge moisture availability in some regions. The LGS is projected to extend, particularly in the southern and southeastern UBNB, enhancing agricultural potential in these areas. However, wheat suitability faces considerable declines; under ssp585, the highly suitable area is expected to drop from 24.21% to 13.31% by the 2080s due to thermal and moisture stress. This study highlights the intricate relationship between agroclimatic variables and agricultural productivity. Integrating GAEZ and CMIP6 projections provides quantified insights into the impacts of climate change on wheat suitability. These findings offer a foundation for developing adaptive strategies to safeguard food security and optimize land use in vulnerable regions.

1. Introduction

Meteorological conditions, such as temperature, precipitation, length of growing season (LGS), and soil moisture, significantly influence agricultural productivity [1]. Climate change has already had a notable impact on key agricultural outputs, particularly for staple crops like wheat, maize, rice, and soybeans. Studies by Rezaei et al. [2] show that global yields of these crops have slowed over recent decades, with wheat and maize experiencing significant declines in certain regions. Rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events—such as heatwaves and droughts—are major contributors to these declines [3,4]. In particular, increased temperatures shorten growing periods, reduce grain filling, and lower yields, especially for wheat and rice [2]. Furthermore, erratic rainfall exacerbates water stress, particularly in semi-arid regions, while excessive rainfall leads to flooding and soil erosion, further threatening agricultural productivity. For example, global wheat yields have declined by approximately 6% over the past 50 years due to climate change [2], with tropical regions facing the greatest losses, intensifying food insecurity.

Temperature, moisture conditions, and LGS are crucial factors in determining land suitability for wheat cultivation. Wheat grows optimally at temperatures between 15 and 25 °C; temperatures exceeding this range can reduce yields by limiting grain filling and accelerating maturation [5]. Similarly, variations in LGS, driven by changes in temperature and moisture availability, influence the timing of wheat cultivation, thus affecting potential yields and land suitability [6]. In the UBNB, climate change is expected to affect wheat suitability spatially, enhancing conditions in some areas while stressing others due to thermal and moisture challenges. For instance, moderate extensions of LGS (5–10 days) could increase wheat productivity by 10–15%, provided water availability remains adequate [7].

Agriculture is highly vulnerable to climate change, but it can also contribute to climate change through unsustainable practices [8]. While the negative impacts on crop productivity are well documented, these effects can be mitigated by adaptive measures such as adjusting planting and harvesting times, crop selection, and increasing irrigation [9]. Moreover, the growing season has been affected by climate change, with warmer temperatures and increased water availability extending the growing season in some regions [10,11,12]. However, the long-term viability of these extended growing periods is uncertain, as plants may adjust to greater interannual climate variations or reduced water availability, potentially leading to declines [10]. Zeng et al. [11] demonstrate that changes in temperature and precipitation not only affect the length of the growing season but also the timing of critical crop stages like flowering and maturation, which can jeopardize crop yields.

In Ethiopia, the average annual temperature has risen by 1.7 °C between 1960 and 2020, with the most significant warming occurring between July and September [13]. Projections indicate that by the 2030s, temperatures will increase by 1.5 °C, by 1.8 °C by the 2050s, and up to 3.7 °C by the 2080s under moderate-to-high emissions scenarios [14]. Along with this warming trend, Ethiopia has experienced notable year-to-year variability in rainfall, with significant regional inequalities [15,16]. While some models predict a slight increase in overall precipitation by 2080, others forecast a decrease in the 2030s and 2050s [15,16]. In the UBNB, temperature increases vary, with the western and southern regions projected to experience more pronounced warming (up to 4.85 °C by the 2080s under SSP585) than the northern and eastern regions [17]. Although projections suggest an increase in heavy precipitation events by 20% by the end of the century, these events could exacerbate soil erosion, reduce topsoil fertility, and increase flooding risks, which may damage wheat crops, delay planting, and lower productivity [18]. Consequently, climate change has had and will continue to have a significant influence on the country’s water resources, agricultural output, livestock husbandry, and public health [19].

The UBNB has seen some increase in annual precipitation [20,21,22,23], in most of its parts [24]. In the southern and southeastern regions, it could exceed 1000 mm by the end of the century [25]. However, the central and northern regions remain vulnerable to periodic dry spells. Climate models show significant spatial heterogeneity in rainfall patterns, with the southern regions benefiting from higher precipitation, while northern areas face more modest changes and remain susceptible to drought. As temperature increases, evapotranspiration is expected to counteract any potential benefits from slight increases in precipitation [16]. Due to combined thermal and moisture stresses over the UBNB, wheat suitability shows a substantial decline [5]. The region is known for its heterogeneous soil type and topography [15,17,21], which has a great effect on temperature and precipitation. Hence, it is important to incorporate localized data on soil and topography to refine global models and address the unique challenges faced by vulnerable regions. The UBNB’s heterogeneity, encompassing diverse climatic zones and soil types, makes it particularly suited for such integrated analyses [18,21]. Conducting research in the UBNB is significant due to its unique climatic and geographical characteristics, which influence agricultural productivity. The region is vulnerable to climate change, making it essential to understand how these changes affect wheat production. This research can inform local agricultural practices and policies, ultimately contributing to food security in Ethiopia. By leveraging high-resolution data and advanced methodologies, this study seeks to bridge the gap between broad-scale climate projections and localized agricultural needs.

Assessing land suitability for agriculture is essential to understand how climate change will affect crop production. Agricultural land suitability assessment (ALSA) helps predict land potential for crop cultivation, considering both climatic and edaphic factors [26,27,28,29]. The Global Agroecological Zone (GAEZ) approach, used to assess agroclimatic parameters like temperature, moisture, and growing season length, is critical for evaluating land suitability for crops like wheat [30]. This assessment is especially important in regions like the UBNB, where agroclimatic conditions are expected to shift significantly due to climate change. The objectives of this study are to (1) quantify changes in key agroclimatic indicators (temperature, precipitation, and growing season length) under baseline and future climate scenarios (ssp370 and ssp585); (2) evaluate shifts in agroecological zones and their impact on wheat suitability; (3) assess how these changes affect land productivity, especially in rainfed systems; and (4) propose adaptive strategies to mitigate climate change impacts and optimize land use. The findings will provide actionable recommendations for policymakers to enhance food security and agricultural resilience in the UBNB.

2. Methodology

This study integrates climate projections with agroclimatic modeling to assess the impact of changing temperature and precipitation on wheat suitability in the UBNB (Figure 1). Climatic data for the baseline (1981–2010) and future periods (2020s, 2050s, and 2080s) were obtained from five CMIP6 global climate models under two scenarios: ssp370 and ssp585. These projections were downscaled (30 m resolution to analyze thermal regime, humidity index, and LGS, and 1 km to comply with GAEZ framework) and bias-corrected for local-scale assessments. Agroclimatic indicators, including growing degree days (GDD) and the moisture availability index (P/PET), were used to evaluate temperature and precipitation impacts on wheat suitability. LGS was also analyzed for potential shifts in crop viability. The GAEZ framework was applied to integrate these indicators into a spatial model of wheat suitability. Future scenarios of wheat production were generated, providing insights into challenges and opportunities for agricultural adaptation.

2.1. Data

These GCMs were chosen according to the priority of the Intersectoral Impact Model Intercomparison Project (ISIMIP) model [31]. After bias-correcting the data using a trend-preserving bias correction method as outlined in Lange (2019), the data were downscaled to 1 km using a linear interpolation method. Using two common socioeconomic pathways, ssp370 (SSP3-RCP7) and ssp585 (SSP5-RCP8.5), data from the Climate Model Intercomparison Project version 6 (CMIP6) were used to assess agroclimatic indicators in the UBNB [31]. Potential evapotranspiration (PET or ET0) was computed using the Penman–Monteith equation, and the GAEZ documentation provides a thorough methodology for this calculation [30]. The data sources used in this particular study is presented in Table 1 below.

2.2. GAEZ and Agroclimatic Indicators

2.2.1. GAEZ

The Food and Agricultural Organization (FAO) of the United Nations and the International Institute for Applied Systems Analysis (IIASA) collaborated to develop the GAEZ database and methodology, a modeling tool used in the evaluation of past and future agroclimatic conditions [30]. AEZ uses agronomic knowledge to assess the suitability of agroclimatic conditions and the availability of land resources, investigate possible farm-level management strategies, and project crop yields. It uses large-scale spatial biophysical, socioeconomic, and meteorological datasets to distribute calculations at particular gridded intervals across the whole region it is analyzing. Following the compilation of land resources and agroclimatic inventories, assessments are conducted to determine crop suitability for specified input levels and management scenarios, including rainfed and irrigated conditions, and to calculate expected achievable crop yields in the specific agroecological setting. Climate, soil composition, topographical features, and current land use are elements that constitute the definition of agroclimatic conditions and land resources. Daily variations in temperature and moisture levels are necessary for simulating crop growth dynamics within the AEZ.

2.2.2. Thermal Regime

It is essential to take temperature into account to control crop growth and development. The influence of temperature on crop growth within each grid cell of an AEZ is determined by the thermal patterns, which can be reproduced by temperature accumulation and the length of the temperature accumulation phase. For every grid cell, the total reference temperature accumulation was calculated by adding the daily mean temperature (Ta) on days where Ta was higher than the designated threshold temperatures (5 and 10 °C). The length of the temperature accumulation period was calculated as the number of days in a particular year where Ta was higher than the predetermined threshold [30,34].

2.2.3. Annual Moisture Availability Index (P/PET)

The moisture availability index, also known as the humidity index, assesses environmental conditions affecting ecological and agricultural activities. It measures the relationship between atmospheric moisture and its capacity to hold more at a given temperature. This index plays a crucial role in crop development, production potential, and water management. High humidity indicates more moisture availability, which can enhance plant transpiration, maintain soil moisture, and increase crop yield. Conversely, low moisture availability can lead to water shortages, reduced yields, and greater drought vulnerability.

The index compares precipitation to the evaporation required by a reference crop, using the Penman-Monteith equation to estimate ETo. An index value of 100 indicates equilibrium between precipitation and evaporative demand, while values below 100 signal a water deficit, and values above 100 indicate a precipitation surplus [30].

2.2.4. Length of the Growing Season

The length of the growing season (LGS) is a key factor in determining a land’s agroclimatic productivity, as it reflects the period when temperature and moisture conditions are favorable for crop growth. In the GAEZ approach, LGS refers to the number of days when the average daily temperature exceeds 5 °C and actual evapotranspiration (ETa) is above 40–50% of ET0, indicating sufficient soil moisture for crop growth [30].

The “temperature growing season” (LGSt) is determined by the number of days when the mean daily temperature exceeds a specific threshold, typically 10 °C, under irrigation conditions. This study assessed the LGSt for the reference period (1981–2010) and future periods (2020s, 2050s, and 2080s) under ssp370 and ssp585 climate scenarios.

2.2.5. Suitability Class Index (SI)

The suitability class index for crops reflects conditions necessary for their growth and potential yield. In Ethiopia, wheat grows best between 1500 and 3300 m a.s.l., with optimal elevations of 2000–2600 m. It requires 400–1200 mm of rainfall and temperatures of 15–25 °C, with the distribution of rainfall during the growing season being crucial [26]. Wheat in Ethiopia is mainly rainfed, with yields ranging from 2.5 to 3 tons per hectare in suitable areas, though this varies with agroclimatic conditions [35]. The growing season typically runs from June to December. Wheat production in the UBNB faces challenges such as erratic rainfall, soil degradation, and increased pest outbreaks due to climate change [25]. Mid-elevation zones with moderate temperatures and reliable rainfall are currently the most productive but are also the most vulnerable to climate shifts, including rising temperatures and rainfall variability [7]. The suitability classes are described in Table 2 below.

Wheat grows well in red, brown, and black clay soils but not in sandy or waterlogging-prone soils. High soil fertility, especially nitrogen and phosphate, is essential, as wheat has moderate salt tolerance [36]. This analysis assumed fallow conditions for soil fertility and high input levels with advanced management practices. Mapped suitability classes were based on the normalized suitability index (SI) from the GAEZv4 documentation [30]:

$SI={\displaystyle \frac{\left(90\times VS+70\times S+50\times MS+30\times mS+15\times vmS+0\times NS\right)}{0.9}}$

where SI is suitability index, VS, S, …, NS are the extents of the area in a grid cell assessed as very suitable (VS), suitable (S), moderately suitable (MS), marginally suitable (mS), very marginally suitable (vmS), or not suitable (NS), respectively.

The following table shows these classes and their respective descriptions.

Table 2

Suitability classes and their descriptions.

Acronym	Suitability Description
VS	Very suitable land: prime land offering the best condition for crop production
S	Suitable land: good land for crop production
MS	Moderately suitable land: Moderate land with substantial climate and/or soil/terrain conditions for crop production
mS	Marginally suitable land: less suitable for the production of crops
vmS	Very marginally suitable land: very little (low) suitable land for crop production
NS	Not suitable land: not suitable for the production of crops

3. Results

3.1. Climate Change over the UBNB

Figure 2 and Figure 3 show the basin-wide fluctuations in temperature and precipitation within the context of two SSP scenarios (ssp370 and ssp585) in comparison to the reference period (1981–2010) climate. These differences were demonstrated by the annual total average precipitation and daily mean temperature. The eastern, northern, and central sectors of the basin are predicted to experience comparatively fewer increases in temperature than the remainder of the basin. The southern and western regions of the basin are expected to exhibit more prominent warming trends. Daily mean temperature indicators and a warming trend were clearly observed at every location in both scenarios. The projected increases in the mean annual daily mean temperature across the basin under the ssp370 and ssp585 scenarios are expected to range from approximately 1.13 °C (2020s) to 4.10 °C (2080s) and from approximately 1.10 °C (2020s) to 4.85 °C (2080s), respectively. Specifically, in the near (2020s), far (2050s), and distant future (2080s) of the century, ssp585 is more likely than ssp370 to experience a warming trend. Compared to that of ssp370, a temperature increase of approximately 0.7 °C is predicted to occur by the 2080s with ssp585.

There were notable differences between the basin zones in the predictions made from these scenarios. Simulations of both scenarios showed that precipitation levels will increase everywhere, with the eastern, central, northern, and southern regions expected to experience the greatest increases. However, the western area of the basin is anticipated to have a comparatively mild increase in precipitation levels, in contrast to other areas.

Simulations of both scenarios have shown that precipitation will inevitably increase in future decades. By the end of the century, compared to those in previous eras, the increase in precipitation levels was predicted to surpass 1000 mm. However, these elevated values are expected to be restricted to specific regions within the UBNB. For most of the UBNB, however, a decrease in precipitation was predicted in the 2020s under both scenarios and in the 2050s under the ssp370 scenario (see Figure 3).

3.2. Impacts on Moisture and Thermal Conditions

Within the context of two simulated climatic scenarios (ssp370 and ssp585), Figure 4 and Figure 5 depict the spatial distribution (differences) of the temperature sum exceeding 10 °C and the humidity index (HI) under (from) the baseline period (1981–2010). For crops to progress through their growth stages, there must be sufficient temperature. These data indicate that the thermal and moisture conditions in the basin are potentially impacted by climate change.

The geographical distribution of TS10 throughout the UBNB under the reference (1981–2010) environmental characteristics is shown in the first panel of Figure 4. Altitude and latitude fluctuations dominate the TS10 distribution, with minimal values found close to 1000 °C in the eastern sector and maximum values found far above 6500 °C in the western portions of the UBNB.

The mean difference in TS10 over the region from the reference period (1981–2010) under the ssp370 and ssp585 forecasts is shown in the middle and lower panels of Figure 4. The projected mean TS10 is expected to range from approximately 1500 °C in the eastern sector to over 6500 °C in the western portions of the UBNB by the 2080s. The western region shows the highest increases with differences of up to 1000 °C under ssp585 scenarios by the 2080s. A general increase in TS10 across the board is expected; however, there is notable variation between zones. According to the model simulations, the TS10 increase is most noticeable in the western region in both cases, whereas the growth rates in the eastern and northern regions are significantly lower. There are fewer differences between the two scenarios in the 2020s and the 2050s than in the 2080s. In addition, under ssp370, TS10 increases by an average of 10–15% by the 2050s, while under ssp585, this rise extends to 20–25% by the 2080s.

The HI, or precipitation to potential evapotranspiration (PET) ratio, shows a clear gradient under the baseline climate (Figure 4, upper panel), with the southern and central sectors of the basin exhibiting the highest values. On the other hand, the eastern section and some parts of the northern and western zones had the lowest recorded levels. The results also showed that future moisture levels throughout the UBNB will be affected by the predicted consequences of climate change. As depicted in the lower panels of Figure 5, the projections for both scenarios indicate a general increase in the ratio of PET to precipitation in every basin region. The results imply that the projected increase in precipitation levels is significant enough to reduce the dry environment in the area in the 2020s, 2050s, and 2080s.

The difference in HI between the predicted climates under the ssp370 and ssp585 scenarios and the reference period (1981–2010) climate is shown in the lower panels of Figure 5. The ssp585 scenario predicts a significant increase in the HI (41). It is anticipated that in contrast to other basin regions, the southern region will show a notable change in HI with respect to the reference period. Conversely, it is expected that the HI values will change the least in the eastern and western regions.

3.3. Impacts on Length of Growing Season

In Ethiopia, precipitation has a greater influence on the length of the growing season than does temperature. Using the GAEZ concept created by IIASA/FAO, the thermal growing season is essential for defining the AEZs of the UBNB. It is acknowledged as a major agroclimatic parameter. Figure 6 (top panel) shows the spatial distribution of the GSL under baseline meteorological conditions. The graph indicates that much of the basin has a growing season longer than 250 days each year, especially in the eastern and southern sections. In contrast, several regions in the central region experienced a low thermal growth period. In the Eastern part of the basin, the duration of the growing period ranged between 175 and 250 days.

The middle and bottom panels of Figure 6 illustrate how the GSL changed from the reference baseline (1981–2010) climate under the ssp370 and ssp585 scenarios, respectively. The panels show constant growth in the LGP throughout all locations, with significant changes anticipated in both scenarios for the 2080s. Based on all the model predictions, it is clear that the southern and southeastern portions of the basin are expected to experience the largest increase. Under the ssp370 scenario, the mean LGS in the UBNB is projected to increase by 15–20 days in these areas by the 2050s compared to the baseline period (1981–2010). This extension is driven by a combination of earlier planting opportunities due to warmer spring temperatures and the later onset of frost in the autumn. Under ssp585, the LGS could increase by up to 30–40 days in some areas by the 2080s, particularly at higher elevations. These changes result from a projected temperature rise of 2.5 °C to 4.85 °C in these regions. However, in drier regions of the UBNB, the increasing variability of precipitation may limit the benefits of an extended LGS. The increase in the mean daily mean annual temperature plays a vital role in the increase in the GSL over the southern portion of the basin. However, under both scenarios, the locations that are widely distributed throughout the basin (highlighted in blue) are predicted to experience a decrease in GSL in every study year, albeit to differing degrees of severity.

3.4. Impacts on the Land Suitability of Wheat

The largest share of suitable land mass for wheat across the UBNB, as depicted in Figure 7 and Table 3, was in the high-suitability class (24.21%), followed by the good-suitability class (22.76%). Most of the northern, central, and southeastern parts of the basin are comfortable for wheat cultivation but have different suitability classes. The suitability of the landmass under the changing climate showed a shift to the eastern and southeastern parts of the basin under both scenarios. The increases in precipitation and HI increased the suitability of the land for wheat cultivation in the respective regions.

The very high, medium, and unsuitable classes show an increase under the ssp370 scenario in the respective study year categories. The remaining area exhibited a decrease in landmass for the sake of comfort during wheat cultivation. Under the ssp585 climate scenario, high and good suitability classes will decrease the suitable landmass share for wheat. The landmasses suitable for cultivating wheat will lose suitability in 2050 (5.14%) and in the 2080s (10.8%) under scenario ssp585. This is due to the thermal and moisture stresses, in addition to the change in the thermal growth period, as illustrated in the above Sections. This implies that there will be a high yield loss due to uncomfortable agroclimatic conditions as a result of the changing climate over the basin in the coming decades.

4. Discussion

In this study, we compared the basic agroclimatic conditions of the UBNB to those predicted for the next few years (2020s), the middle of the 2050s, and the end of the 2080s under two distinct SSP scenarios. We are able to examine a number of critical climate variables that are critical for controlling crop growth and production in this investigation. The combination of rising air temperatures and changes in the amount and distribution of precipitation in the future era is anticipated to extend the time available for crop cultivation and may improve the opportunities for multiple cropping systems in most UBNB areas. The findings demonstrate that there have been increasing temperatures and shifting precipitation patterns in the UBNB. Significant temperature increases ranging from 1.13 °C in the 2020s to 4.85 °C by the 2080s under ssp585 scenarios are expected. Precipitation patterns also show spatial variability with an increase of up to 15% in certain southern regions but with higher variability elsewhere. In addition, LGS extends by 15–20 days under ssp370, and 30–40 days under ssp585, by the 2080s in high-elevation areas of the UBNB. Furthermore, wheat suitability declines, with high suitable area reducing from 24.21% to 13.31% by the 2080s under ssp585. According to Mekonnen and Disse [37] and Daba and You [20], an increasing trend in temperature throughout the basin is inevitable with different magnitudes over different regions of the basin. Based on Roth et al., Mengistu et al., and Tariku et al. [38,39,40], the average, maximum, and minimum temperatures in the basin have shown an upward trend and are projected to continue rising in the future. Daba and You [20] showed that, under the RCP4.5 scenario, rainfall is expected to drop by 3.31% and 9.81%, respectively, in the 2050s and the 2080s and by 6.80% and 16.22%, respectively, during the same time periods under RCP8.5. Variability in precipitation is inevitable across and between basin areas. Rainfall variability has shown an increasing tendency (although not statistically significant) from 1987 to 2016, with a projected decline in the upper Blue Nile basin [40,41] and a diminishing trend encompassing most of the basin area from 1979 to 2014 [42]. The rise in temperature and shifts in rainfall patterns affect wheat suitability by altering key agroclimatic variables. For example, rising temperatures reduce the suitability of higher-elevation areas by exceeding thermal thresholds, while extended LGS in lower elevations could enhance crop viability. Increased precipitation and moisture indices contribute to better suitability in regions currently constrained by aridity but may pose risks in areas susceptible to waterlogging or soil degradation. These dynamics underline the need for targeted interventions, such as introducing heat-tolerant wheat varieties or improving water management practices, to mitigate regional disparities in agricultural productivity [43,44]. The projected increase in heavy precipitation events has complex implications for wheat production in the UBNB. While higher rainfall volumes may alleviate water stress in previously arid zones, the intensity of these events can lead to waterlogging and reduced root aeration, hindering wheat growth [45]. Furthermore, soil erosion associated with heavy rainfall can degrade land quality, making it less suitable for cultivation over time. Addressing these risks requires investment in erosion control measures, such as terracing or cover cropping, and the development of resilient water management systems to mitigate flooding and optimize soil moisture retention during the growing season [46].

Ethiopia has experienced distinctive climate fluctuations due to its orographic characteristics [47,48,49]. As a result, agroclimatic parameters are unpredictable. The upper and lower elevation zones of the UBNB [50] and the lowland agroclimatic region [51] have shown increased sensitivity to climate hazards. The spatial heterogeneity of climate impacts across the UBNB underscores the need for localized agricultural planning. While increased precipitation in the southern and southeastern regions could improve water availability and extend the growing season, it may not uniformly benefit wheat production. Conversely, drier conditions in the central and northern regions could exacerbate water stress and reduce land suitability. The region-specific findings highlight the importance of tailoring adaptation strategies to local climatic conditions, including irrigation development in drier zones and erosion control measures in areas prone to heavy rainfall [52,53]. It is crucial to stress that the benefits of a longer growing season brought about by higher temperatures may be offset by changes in the pattern of rainfall. This is because a combination of these two variables may cause the amount of moisture to drop during warmer months, especially in areas that are arid or semiarid, where the availability of water has historically been a major barrier to agricultural productivity.

Cropland suitability evaluation is essential for agricultural progress and planning. The effects of climate change threaten sustainable agricultural practices and food resource availability [54]. Additionally, socioeconomic factors like land tenure, irrigation access, market infrastructure, and labor influence agricultural practices significantly in regions such as the UBNB. Secure land tenure encourages investment in soil fertility, while access to markets allows the adoption of advanced technologies to counter climate risks [55,56]. The growing population in developing countries like Ethiopia exerts considerable pressure on underutilized natural resources. A thorough analysis of land suitability is vital before initiating crop cultivation to prevent irreversible land degradation [57]. Limited financial resources and fragmented landholdings hinder smallholder farmers’ adaptation to climate variability. Addressing these socioeconomic issues is essential for promoting equitable agricultural development and food security. Given the limited availability of arable land, expanding agricultural holdings is impractical. Thus, categorizing land suitability is crucial for optimizing cereal production within current land limitations. Incorporating socioeconomic data into land suitability assessments enables agricultural planners to identify areas for targeted interventions that enhance productivity and resilience [58]. For instance, investing in community irrigation can improve water access, while credit and extension services can facilitate the adoption of climate-resilient practices. Assessing land suitability also ensures that crops are cultivated in environments best suited for their growth, maximizing yields while minimizing environmental impacts [30]. By incorporating socioeconomic considerations into agricultural planning, land suitability analysis becomes a powerful tool for fostering sustainable practices, promoting resilience, and securing food supplies for growing populations.

To address the climate-induced changes in agroclimatic variables, the following specific adaptation strategies tailored for the UBNB are recommended. One effective measure is the introduction of drought-tolerant and heat-resistant wheat varieties, such as Triticum aestivum L., which thrive under elevated temperatures and fluctuating moisture levels. These varieties can be particularly beneficial in regions where wheat suitability is projected to decline. Additionally, promoting the cultivation of alternative crops with flexible growing season requirements, such as barley or teff, offers a viable solution to enhance food security [59]. In addition, water management strategies are equally critical. For example, the implementation of efficient irrigation systems like drip irrigation can maximize water-use efficiency in areas with projected decreases in precipitation. Similarly, small-scale rainwater harvesting systems can provide supplemental water resources, especially in drier regions of the basin [35]. Alongside these measures, soil conservation techniques such as terracing, cover cropping, and mulching are essential for mitigating soil erosion caused by heavy rainfall events, which are projected to increase by 20% by the 2080s under SSP585 [60].

Effective policies possess the potential to cultivate a conducive environment for the integration of sustainable practices, foster interdisciplinary collaboration, and harness support from global organizations. It is imperative that policies promote Climate-Smart Agriculture (CSA) through financial incentives and robust institutional frameworks, which are critical for successful implementation [61]. The promotion of methodologies such as crop diversification and precision agriculture can markedly enhance agricultural productivity and resilience [62]. The synergistic collaboration among agriculture, water resource management, and environmental conservation is essential for the formulation of comprehensive adaptation strategies [63]. Involving local communities in the decision-making framework guarantees that policies are customized to meet specific needs, thereby augmenting their efficacy. International organizations have the capacity to extend both technical and financial support, thereby facilitating the global scale-up of CSA practices [62]. National policies must be congruent with the requirements of local communities to ensure the achievement of favorable adaptation outcomes.

Finally, policy and technological interventions can enhance adaptation efforts. Strengthening extension services to educate farmers on climate-smart practices, such as adjusting planting dates based on agroclimatic forecasts, is crucial. Investments in GIS and remote sensing technologies can facilitate real-time monitoring of agroclimatic trends, enabling targeted responses to emerging challenges [64,65]. These strategies collectively underscore the importance of integrating scientific insights into local agricultural practices to ensure climate resilience.

5. Conclusions

This study highlights the significant impacts of climate change on agroclimatic conditions and wheat suitability in the UBNB. Using the GAEZ framework and CMIP6 climate projections under SSP370 and SSP585 scenarios, the analysis reveals substantial warming trends, with mean annual temperatures projected to increase by 1.10 °C to 4.85 °C by the 2080s. These temperature changes are coupled with spatially variable precipitation patterns, where some areas are expected to experience an increase of up to 15% in annual rainfall, while others may face heightened variability, posing challenges to agricultural water management.

The LGS is projected to extend by 15–20 days under SSP370 and by 30–40 days under SSP585 in certain high-elevation regions, presenting opportunities for increased agricultural productivity. However, these potential benefits are counterbalanced by reductions in land suitability for wheat cultivation. The area classified as highly suitable for wheat is expected to decline from 24.21% to 13.31% under SSP585 by the 2080s due to thermal and moisture stresses, signaling a substantial loss of productive land.

The findings underscore the urgent need for adaptive strategies tailored to the region’s diverse agroecological conditions. Key recommendations include introducing heat- and drought-resistant wheat varieties, optimizing water management through advanced irrigation techniques such as drip irrigation, and implementing soil conservation measures to mitigate the risks of erosion from a 20% increase in heavy rainfall events projected by the end of the century. These interventions are essential to offset the adverse impacts of climate change on wheat production.

This research provides a robust foundation for policymakers and agricultural stakeholders to design localized and scalable solutions. By integrating agroclimatic indicators into regional land-use planning and employing continuous monitoring of climate impacts, the UBNB can enhance its resilience to climate variability. Such actions will play a pivotal role in safeguarding agricultural productivity, promoting sustainable development, and ensuring food security in the region amidst evolving climatic conditions.

Author Contributions

W.L.D.—conceptualization, methodology, visualization, writing. T.T.Z.—conceptualization, supervision, review and editing. K.T.—methodology, supervision, review and editing. D.K.M.—supervision, review and editing. M.A.F.—reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data that support the findings of this study are available publicly; the reader should contact the corresponding author for additional details.

Acknowledgments

The authors would like to thankfully acknowledge the FAO/IIASA, Copernicus climate change service, CHELSA, NOAA, Geophysical Fluid Dynamics Laboratory, Met Office Hadley Centre, Max Planck Institute, Institut Pierre-Simon, Laplace, Meteorological Research Institute and Intersectoral Impact Model Intercomparison Project for providing different climatic data and models for research purposes. We are also very grateful to Hawassa University for providing partial funding for this study in collaboration with the Norwegian University of Life Sciences (NMBU).

Conflicts of Interest

The authors declare no competing interests.

Footnotes

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Figures and Tables

Figure 1. Conceptual framework.

Figure 2. Mean annual daily mean temperature in the UBNB. The top panel shows the baseline period (1981–2010), and the middle and bottom panels display changes under ssp370 and ssp585 scenarios, respectively.

Figure 3. Mean annual precipitation in the UBNB. The top panel shows the baseline period (1981–2010), and the middle and bottom panels display changes under ssp370 and ssp585 scenarios, respectively.

Figure 4. Mean annual temperature sum (TS10) above 10 °C for the UBNB. The top panel represents the baseline period (1981–2010), while the middle and bottom panels illustrate changes in TS10 under ssp370 and ssp585 scenarios, respectively.

Figure 5. Spatial distribution of humidity indices (HIs) in the UBNB. The top panel shows the baseline period (1981–2010), and the middle and bottom panels depict changes in HIs under ssp370 and ssp585 scenarios, respectively.

Figure 6. Spatial distribution of the GSL in the UBNB. The top panel represents the baseline period (1981–2010), while the middle and bottom panels show changes in GSL under ssp370 and ssp585 scenarios, respectively.

Figure 7. Spatial distribution of wheat suitability classes in the UBNB under three scenarios: (1) the baseline period (1981–2010), (2) the ssp370 scenario, and (3) the ssp585 scenario. Each panel clearly delineates areas classified from very suitable to not suitable, based on agroclimatic constraints and high agricultural input conditions in a rainfed system.

Table 1

Sources of climate data and models.

Data Source	Description	Time Period	References
CHELSA	High-resolution climatologies for Earth’s land surface areas.	Baseline: 1981–2010 Future: 2011–2041, 2041–2070, 2071–2100 from the below GCMs	[31]https://crudata.uea.ac.uk/cru/data/hrg/ (Access date: 15 July 2024)
C3S Climate Data Store (CDS)	Provided climate data for historical baseline and future periods.	Baseline: 1981–2010 Future: 2011–2041, 2041–2070, 2071–2100	[32]https://cds.climate.copernicus.eu/ (Access date: 15 July 2024)
CRU TS 4.04	Climatic Research Unit Time Series database with high temporal resolution.	Baseline: 1981–2010	[33]https://crudata.uea.ac.uk/cru/data/hrg/ (Access date: 15 July 2024)
GFDL-ESM4	NOAA Geophysical Fluid Dynamics Laboratory Earth System Model 4.	Future: 2011–2041, 2041–2070, 2071–2100	https://www.gfdl.noaa.gov/earth-system-esm4/ (Access date: 15 July 2024)
UKESM1-0-LL	Met Office Hadley Centre Earth System Model 1.	Future: 2011–2041, 2041–2070, 2071–2100	https://ukesm.ac.uk/ (Access date: 15 July 2024)
MPI-ESM1.2-HR	Max Planck Institute for Meteorology Earth System Model.	Future: 2011–2041, 2041–2070, 2071–2100	https://www.mpimet.mpg.de/en/science/models/mpi-esm (Access date: 15 July 2024)
IPSL-CM6A-LR	Institut Pierre-Simon Laplace Climate Modeling Centre Earth System Model.	Future: 2011–2041, 2041–2070, 2071–2100	http://www.ipsl.fr/en/ (Access date: 15 July 2024)
MRI-ESM2.0	Meteorological Research Institute Earth System Model Version 2.0.	Future: 2011–2041, 2041–2070, 2071–2100	https://www.jma.go.jp/jma/en/CPC/ESM/ESM.html (Access date: 15 July 2024)

Table 3

Suitable landmass shares for wheat.

			Historical	ssp370			ssp585
				2020’s	2050’s	2080’s	2020’s	2050’s	2080’s
No	Suitability Class	Index Range	Area (%)sq. km	Area (%)	Area (%)	Area (%)	Area (%)	Area (%)	Area (%)
1	Very high	>85	16,214.39 (9.27)	17,961.04 (10.27)	20,776.54 (11.88)	22,363.02 (12.79)	18,240.61 (10.43)	16,810.71 (9.61)	20,008.59 (11.44)
2	High	70 < SI < 85	42,352.36 (24.21)	35,466.11 (20.28)	32,802.31 (18.75)	31,579.74 (18.05)	38,510.04 (22.02)	32,768.68 (18.73)	23,286.19 (13.31)
3	Good	55 < SI < 70	39,811.33 (22.76)	42,416.98 (24.25)	39,377.18 (22.51)	37,503.36 (21.44)	39,124.38 (22.37)	34,044.71 (19.46)	29,764.52 (17.02)
4	Medium	40 < SI < 55	17,690.37 (10.11)	18,411.13 (10.53)	19,391.01 (11.09)	19,933.63 (11.40)	19,128.46 (10.94)	20,352.43 (11.64)	18,358.97 (10.50)
5	Moderate	25 < SI < 40	22,057.93 (12.61)	23,485.63 (13.43)	23,257.17 (13.30)	23,886.92 (13.66)	21,377.65 (12.22)	23,877.37 (13.65)	23,134.38 (13.23)
6	Marginal	10 < SI < 25	3586.83 (2.05)	3593.85 (2.05)	4283.22 (2.45)	3862.69 (2.21)	3760.29 (2.15)	6174.85 (3.53)	9568.86 (5.47)
7	Not suitable	<10	1307.03 (0.75)	1685.03 (0.96)	3132.45 (1.79)	3890.74 (2.22)	2878.63 (1.65)	8991.41 (5.14)	18,898.33 (10.80)
8	No data		29,213.47 (16.7)	29,213.47 (16.7)	29,213.47 (16.7)	29,213.47 (16.7)	29,213.47 (16.7)	29,213.47 (16.7)	29,213.47 (16.7)
9	Water		2676.99 (1.53)	2676.99 (1.53)	2676.99 (1.53)	2676.99 (1.53)	2676.99 (1.53)	2676.99 (1.53)	2676.99 (1.53)

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