Climate change affects rainfall seasonality and timing in maize-based agroecological zones of Ethiopia

Climate change affects rainfall seasonality and timing in maize-based agroecological zones of Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate change affects rainfall seasonality and timing in maize-based agroecological zones of Ethiopia Dereje Birhan, Kindie Tesfaye, Belay Simane, Alemu Tolemariam, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8921665/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Rainfed subsistence agriculture is vital for food security in East Africa, particularly in regions where maize is the dominant crop. Its success depends heavily on rainfall patterns, making it vulnerable to climate variability and change. This study analyzes the variability in Rainfall Onset Date (ROD), Rainfall Cessation Date (RCD), and Growing Season Length (GSL) across major maize-growing agroecological zones (AEZs) in Ethiopia. It also examines the potential impacts of future climate scenarios and identifies adaptation strategies to reduce risks to maize production. High-resolution historical data (1981–2019) and projections from CMIP6 models under the SSP5-8.5 scenario (2020–2049) were used. The results show significant variation in ROD, RCD, and GSL between AEZs and climate periods. Future projections indicate delayed ROD (by 10 to 49 days), earlier RCD (by 6 to 95 days), and a shorter GSL (by 10 to 71 days), with high interannual variability. These shifts may expose maize crops to water stress during critical growth stages, increasing drought vulnerability and reducing yields. To sustain production, agricultural practices must be reevaluated. Recommended adaptation strategies include adjusting planting dates, adopting drought-tolerant and early-maturing maize varieties, and improving irrigation efficiency. Revising cropping calendars and promoting collaboration among policymakers, researchers, and farming communities are essential to developing effective, site-specific responses. These efforts are critical to strengthening the resilience of maize-based farming systems and ensuring food security under changing climatic conditions in Ethiopia and the wider East African region. Adaptation agroecological zones climate-smart agriculture East African farming systems maize yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Maize (Zea mays L.) is a critical cereal crop and cornerstone of food security in sub-Saharan Africa (Mulungu and N. Ng’ombe, 2020 ). It is cultivated across a diverse range of environments, from dry lowlands to highlands (Cairns et al., 2013 ). In Ethiopia, maize is grown extensively across all regions with mid-altitude areas being the most predominant (Chawarska et al., 2009 ). Similarly, Rwanda’s wet mid-altitude areas are identified as key maize-growing zones. In Ethiopia, maize is the most widely cultivated crop, ranking first in productivity and second in area coverage (Mohammed, 2021 ). Its importance to food security surged following the 1984 drought famine (Abate et al., 2015 ), and today, over 11 million smallholder households grow maize in Ethiopia (CSA, 2022 ). Between 1995 and 2021, the area of maize production expanded significantly from 1.2 million hectares to 2.5 million hectares, and productivity increased from 1.19 t/ha to 2.63 t/ha (CSA, 2018 , 2016 , 2015 ). This doubling of productivity within two decades is attributed to the increased adoption of improved maize varieties, expanded extension services, and favorable weather conditions in major maize-growing regions (Abate et al., 2015 ). However, despite these gains, maize production remains highly variable across years, with recent years showing greater fluctuations in yield and coverage. In Rwanda maize is the third most important crop covering 10% of the total cultivated land. It is predominantly grown by smallholder farmers (WFP, 2021 ). Since 2003, maize production has tripled, driven by rising demand (Chrysostom, 2014 ) and the introduction hybrid maize varieties (Claude, 2017 ; Context Network, 2016 ), with the government using seed and fertilizer subsidies to encourage its adoption (Context Network, 2016 ). Government initiatives, including seed and fertilizer subsidies, have further boosted maize production (Ngango and Hong, 2021 ), with the productivity of maize reaching 1.46 t/ha (National Institute of Statstics of Rwanda, 2019 ). Moisture availability is a key determinant of the maize-growing season in tropical regions. Rwanda experiences two distinct growing seasons: Season A, the main maize-growing season in the country (Agriterra Report, 2021 ) (September to January) and Season B (February to May), with Season A being the main maize-growing period (FAO, 2019 ). Despite its increasing coverage and productivity relative to with other cereals, maize yield gaps in Ethiopia remain substantial, largely due to factors such as limited access to capital, small farm sizes, economic constraints, and poor market access (Mohammed, 2021 ). Low soil fertility, high input costs, and early cessation of rainfall are major production challenges, compounded by disease (Worku et al., 2013), insects pests (Friesen and Palmer, 2002 ; Gezahegn et al., 2018 ), parasitic weeds (Degebasa et al., 2022 ), and genetic factors (Senapathy, 2021 ). Maize lethal necrosis disease, in particular, can cause up to 100% yield in many parts of Ethiopia (Demissie et al., 2016 ). Climate change and variability are also major biophysical constraints affecting maize production in Sub-Saharan Africa. The region’s vulnerability to climate change is heightened by its dependence on natural resources and rained agriculture both of which are highly sensitive to climate variability (World Bank, 2009 ). In Rwanda, the 2022 Season A in the cool humid region was characterized by late rains and poor distribution, resulting in low maize sector performance (Rwanda Meteorology Agency, 2022). Projections under RCP8.5 scenario suggest that by 2035, changes in rainfall timing could reduce maize by up to 8% maize yield reduction in season (Herve, 2019 ). Climate change is expected to reduce maize yields by 5%–25%, particularly in moist lowland and semi-arid areas of Ethiopia (Ginbo, 2022 ; Tesfaye et al., 2015 ; Stuch et al., 2021 ). However, certain agroecological zone (AEZs) such as tepid humid mid-highland, sub-humid mid-highland, moist highland, and per-humid area of Ethiopia may experience maize yield gains 5%–50% due to favorable climatic conditions (Tesfaye et al., 2015 ; Stuch et al., 2021 ). Climate change is projected to significantly impact maize production in Ethiopia by altering land suitability. Projections indicate that some areas in arid and semi-arid highlands may lose maize-producing capacity by the 2050s (Tesfaye et al., 2015 ). Furthermore, studies suggest a potential 13% reduction in maize GSL by the 2030s, leading to a 3.6% reduction in maize yield (Araya et al., 2015 ). High interannual fluctuations in area coverage and productivity, influenced by rainfall timing and seasonality, could further exacerbate yield reductions (Kassie et al., 2014 ). Climate change is intensifying and contributing to food insecurity in the East African region, affecting economic growth and increasing poverty levels (Baptista and Farid, 2022 ). This is largely due to the vulnerability of rainfed crop production systems, which are particularly sensitive to rainfall variability and emerging climate trends (Ademe et al., 2021 ; D. Ademe et al., 2020 ; Birhan et al., 2022 ; Mellander et al., 2013 ; Urgessa, 2014 ), and limited climate information access (Gbangou et al., 2019 ). Seasonal climate variability negatively impacts farm yields, food availability, and income, especially among small-scale agricultural producers, where maize is a critical component of food production (Guido et al., 2020 ). Challenges in adaptation are compounded by low access to farm inputs and the diverse topographic gradients of the region (Ademe et al., 2021 ). Addressing these challenges required localized and agriculturally appropriate units of analyses to better understand and respond to the impacts of large-scale climate variability and change to consider their impacts on at farm levels. Smallholder farmers in the region rely heavily on the timing of rainfall, including ROD, RCD, and GSL for planning crop production (Atiah et al., 2021 ; Mugalavai et al., 2008 ). These parameters are crucial for determining planting dates, input distribution, and land preparation, and have a significant impact on crop yields (Gbangou et al., 2019 ; Mugalavai et al., 2008 ; Sarr, 2012 ). Variability in ROD and GSL, in particular, affects what and when to plant, making accurate prediction of these events essential for successful crop production (Ademe et al., 2021 ; Amekudzi et al., 2015 ; Guido et al., 2020 ). Several climatic factors, such as seasonal rainfall amount, intra-seasonal rainfall distribution, ROD, and RCD, influence crop growth and development, thereby determining the crop production calendar. ROD is especially critical as it dictates the planting time (Marteau et al., 2011 ), and delays in ROD are a reliable indicator of terminal drought in many food-insecure regions of Sub-Saharan Africa (Shukla et al., 2021 ). Uncertainty in ROD and RCD can lead to false planting dates, poor growth, crop failure, and lower yields (Akinseye et al., 2016 ). Early or late planting impacts growth, yield, and farm productivity (Ademe et al., 2021 ; Basu et al., 2016 ; Eggen et al., 2019 ; Selvaraju, 2011 ). Therefore, rainfall timing and seasonality are key factors in determining crop types and productivity (Suryabhagavan, 2017 ; Viste et al., 2013 ). Accurately predicting the start of the growing season under changing climate conditions can help decision-makers and farmers align planting dates with predicted ROD, enhancing the feasibility and sustainability of rainfed agriculture (Amarasingha et al., 2015 ). A lack of objective determination can lead to a significant mismatch between the actual and expect RODs, resulting in false start dates for the upcoming farming season (Ademe et al., 2021 ; Guido et al., 2020 ), which in turn brings unmet expectations for the ROD. Previous studies in western Kenya shown that rainfall beginning dates range from early March to late April (Mugalavai et al., 2008 ). In the Ethiopian highlands, rainfall timing and seasonality in exhibit significant interannual variability (Ademe et al., 2021 ), which greatly impacts crop yield (Eggen et al., 2019 ). Existing ROD, RCD, and GSL studies often focus on fragmented geographical locations, political boundaries, or past events, which may not accurately predict future climate conditions. However, the timing and characteristics of the rainy season are shifting, with projections indicating higher precipitation intensity and longer dry spells (Allan et al., 2020 ; Funk et al., 2019a ; Wainwright et al., 2021 ). Unpredictable rainfall timing and seasonality could severely impact crop harvests and food supply (Rockström et al., 2010 ), emphasizing the need for objective information to improve decision making. Thus, this study aims to analyze the observed (1981–2019) ROD, RCD, and GSL and their future (2020–2049) changes in maize-based systems in Ethiopia. The results provide tailored information to support decision making, benefiting crop growers, breeders, and policymakers in the region. 2. Data and Methods 2.1. Unit of analysis The agroecological zone (AEZ) approach was employed as the unit of analysis, grouping geographical areas with similar climatic conditions that influence their suitability for rainfed agriculture. The study adapted the 2005 FAO agroecological zone classification from the GAEZ v4 Data Portal (fao.org). While maize is cultivated across all 18 conventional AEZs, most of the maize production in a subset of these zone. Developing climate profiles and intervention packages for all maize AEZs presented complexities. Therefore, maize-producing AEZs were regrouped into six clusters based on similarities in rainfall and temperature. Clustering fragmented and patchy maize-producing areas is advantageous for generating decision-making information that is broadly applicable to most maize-producing livelihood systems. Accordingly, moist AEZs in mid- and high-altitude maize-growing areas are designated as 'moist highlands' (MH). Moisture-sufficient AEZs located at low altitudes are referred to as 'moist to sub-humid lowlands' (MSHL). Moisture-deficient AEZs are grouped into 'semi-arid lowlands' (SAL) and 'semi-arid mid-highlands' (SAMH), corresponding to semi-arid lowlands and semi-arid highlands respectively. Low altitude AEZs that are warm and sub-moist are combined and labeled as 'warm sub-moist lowlands' (WSML). AEZs in mid-altitude areas with high moisture and high humidity are clustered and referred to as ‘ tepid-humid mid-highlands ’ (THMH). In Rwanda, only two AEZs that dominantly involved in maize production were targeted for this study (Fig. 1 ). 2.2. Data type and sources Daily rainfall data at a resolution of 0.05°×0.05° from the period 1981–2020 were obtained from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS; Funk et al., 2014 ). Similarly daily temperature data at the same resolution for the period 1981–2016 were taken from the Climate Hazards Centre Climate Infrared Temperature with Stations (CHIRTS) dataset (Funk et al., 2019). Future rainfall and temperature data were derived from the ensemble mean of eight global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6) products, selected for their ability to accurately represent rainfall and temperature patterns, seasonality, and climate teleconnections with ENSO (Table 1 ). Data were generated for two emission scenarios: medium forcing (SSP2-4.5) and strong forcing (SSP5-8.5). Preliminary analysis indicated that both scenarios produced similar results (Appendix Table 1), and results obtained only from the strong forcing scenario was considered for this report. Literature also supports the similarity of results from the two scenarios until 2050 (Arnell, 2004 ; Feleke et al., 2023 ; Levy et al., 2004 ). Table 1 The Coupled Model Inter-comparison Project Phase 6 (CMIP6) models used in this report No Model Resolution Institution 1 ACCESS-CM2 1.875°×1.25° Commonwealth Scientific and Industrial Research Organization (Australia) 2 BCC-CSM2-MR 1.125°×1.125° Beijing Climate Center (China) 3 CNRM-CM6-1 1.4° × 1.4° Météo-France and the European Center for Medium-range Weather Forecast 4 EC-Earth3-Veg-LR 0.70°×0.70° European Center Earth Consortium (Europe) 5 GFDL-ESM4 1.25°×1° Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration (USA) 6 MIROC6 1.41°×1.41° Japan Agency for Marine-Earth Science and Technology (Japan) 7 MPI-ESM1-2-LR 1.875°×1.875° Max Planck Institute for Meteorology (Germany) 8 UKESM1-0-LL 1.875° x 1.25° Met Office Hadley Centre A quantile delta mapping (QDM) statistical bias correction approach was employed in the analysis to reduce the influence of model systematic errors (Cannon et al., 2015 ). Before applying the bias correction, the coarse-scale GCM data were interpolated onto the observation grid, and, if necessary, the time series were linearly adjusted to fit the regular Gregorian calendar (Hempel et al., 2013). For precipitation, a frequency adaptation is used to match the model output's dry-day frequency with the observations (Themeßl et al., 2012 ). 2.2. Defining the rainfall onset date, cessation date and growing season length There are many definitions for identifying the ROD and RCD (Omay et al., 2023 ). In most cases, ROD is either considered the start of the rainy period, based on rainfall data, or it is identified using the evapotranspiration (ET) fraction approach. This approach defines the ROD as the optimal date that ensures sufficient soil moisture during planting and early growing periods to avoid crop failure after sowing (Marteau et al., 2011 ; Mugalavai et al., 2008 ). It requires information on both rainfall and evapotranspiration, as well as to soil characteristics of the study area. A normal cropping period is defined as one where precipitation exceeds potential ET (PET). Such a period meets the ET demands of crops and recharges soil moisture (FAO 1978; 1977; 1986). Accordingly, ROD is defined as the first day when the actual-to-PET ratio is greater than 0.5 for seven consecutive days, followed by a 20-day period in which plant available water remains above wilting in the root zone. RCD is defined as the first day when the actual-to-PET ratio falls below 0.5 for seven consecutive days, followed by 12 consecutive non-growing days in which plant-available water remains below wilting in the root zone. The GSL is then calculated as the total number of days from the rainfall ROD to RCD. This method requires daily rainfall and evapotranspiration data, which are often unavailable in many parts of East Africa. As a result, we supplemented our analysis with monthly rainfall and temperature data for each agroecosystem. The monthly evapotranspiration data were calculated from the monthly temperature data for the average latitude of each agroecosystem using the Hargreaves method with the SPEI package in R (Raziei and Pereira, 2013 ; Santiago Begueríae, 2023 ). Monthly values of rainfall and PET were plotted, and the start and end of the growing season were defined as the periods when rainfall exceeds or falls below half of PET, respectively. Planting does not begin immediately when rainfall exceeds half of PET, as several days are required for land preparation. Similarly, crop growth may continue for a few days after rainfall falls below 50% of PET due to residual soil moisture, although this duration varies depending on soil type. Thus, the growing season begins a few days after rainfall exceeds half of PET and lasts until a few days after rainfall drops below half of PET. 3. Results 3.1. Analysis of rainfall timing and seasonality in maize growing areas Understanding rainfall patterns at different spatial scales is crucial for optimizing agricultural practices, mitigating risks, and ensuring food security in regions heavily dependent on agriculture. Such insights enable farmers to make informed decisions regarding planting, irrigation, and harvesting, thereby maximizing yields and minimizing losses. In this context, the results of this analysis are presented across the entire maize-growing landscape, followed by detailed presentations and interpretations of the results within specific AEZs. 3.1.1. Rainfall onset date (ROD) Early ROD in Ethiopia begins in the southwest around the mid-February, followed by the southeast and central areas of the country (Fig. 2 a). as the rain arrive, they bring much-needed relief to the dry, parched lands, moistening the soil and enabling crop growth. In 2035, most maize-growing areas in Ethiopia are projected to experience a significant delay in rainfall onset, except for a few locations in the central and northern parts of the country (Fig. 2 b). Notably, areas found in the southwest and eastern parts of the country may experience delays of more than 40 days in rainfall onset compared to the baseline period. 3.1.2. Rainfall cessation date (RCD) The pattern of RCDs in Ethiopia largely mirrors that of RODs, with few exceptions in the southwest, where the southern regions experience an earlier start and delayed end to the rainy season (Fig. 2 c). The RCD typically progresses from southward and eastward along the central part of the country, then slowly moves southwestward and westward, concluding around the end of October, with the final cessation of short autumn rains in the extreme southwest by the end November. In 2035, RCD is projected to shift in Ethiopia, with some areas in the southwestern, western, southeastern, and northeastern maize-growing regions experiencing a later cessation date (Fig. 2 d). 3.1.3. Growing season length (GSL) The GSL in Ethiopia's maize-dominated areas ranges from 35 to 288 days. The southwestern and eastern highlands enjoy the longest GSL, and the southern, southeastern, and northeastern parts of the country have the shortest season (Fig. 2 e). Variations in GSL across Ethiopia are influenced by altitude, rainfall patterns, and temperature. Longer seasons in the southwestern and eastern highlands contribute to higher maize yields, while shorter seasons in the southern, southeastern, and northeastern regions present challenges to maize production and productivity. Projections indicate a reduction in GSL across most maize-growing areas in Ethiopia, except for some parts of the western, southwestern, southeastern, and northeastern regions (Fig. 2 f). These reductions are attributed to climate change impacts. This reduction may exacerbate existing issues of malnutrition and poverty. Overall, projections for both countries are characterized by a delayed onset, earlier cessation, and a general shrinking of GSL in most maize-dominated AEZs (Fig. 2 ). 3.2. Analysis of rainfall timing and seasonality at agroecological zone 3.2.1. Rainfall onset date (ROD) Data summarized by the main maize-growing AEZs in Ethiopia indicate that the median mean ROD ranges between March 15 and April 29, with the earliest ROD found in THMH (March 15) and SAL (March 25) AEZs and the late ROD found in MSHL AEZ (Fig. 3 a). AEZ-level projected changes in major maize-dominated areas of Ethiopia indicated that ROD would occur later compared to the baseline period in all AEZs, though the magnitude varies (Fig. 4 ). In Ethiopia, ROD will be delay by 12 to 46 days by 2035 compared to the baseline period (Fig. 4 ). These findings underscore the importance of the importance of understanding regional characteristics to tailor solutions effectively. 3.2.2. Rainfall cessation date (RCD) RCDs across Ethiopia’s major maize-growing AEZs ranges from September 24 to November 7, with the earliest and latest cessation dates found in the SAMH (September 24) and MSHL (November 7) AEZs, respectively (Fig. 3 b). A summary of the change in RCD across Ethiopia’s maize-dominated AEZs shows that all AEZs, except THMH, experienced earlier cessation dates with the most significant shifts in the WSML (earlier by 95 days) and SAMH (earlier 68 days) zones compared to the bassline period (Fig. 4 ). 3.2.3. Growing season length (GSL) In Ethiopia, the GSL at the AEZ level ranges from 122 days in SAMH AEZs to 208 days in THMH AEZs (Fig. 3 c). Projections indicate that the GSL will generally decrease compared to the baseline period, though the magnitude of changes varies among AEZs. SAL and WSML AEZs are projected to experience the greatest shrinkage in GSL by 2035, with reductions of 71 and 62 days, respectively, compared to the baseline period. During the baseline period, ROD showed low to moderate interannual variability, with THMH, SAL, and SAMH AEZs in Ethiopia and CSH AEZs in Rwanda exhibiting moderate variability in ROD (Fig. 5 a). Except for the SAMH AEZ, which experiences moderate variability, other AEZs showed low variability in RCD (Fig. 5 b). In 2035, ROD variability is expected to decrease overall, except in the WSML and SAMH AEZs, where only one AEZs (SAMH) will exhibit moderate variability. THMH and SAL AEZs are projected to shift from moderate to low variability in ROD compared to the baseline period (Fig. 5 ). RCD variability is projected to increase in 2035 across all AEZs (Fig. 5 b). Four AEZs are expected to experience moderate RCD variability, with a general increase in variability compared to the baseline period. GSL variability is expected to increase across all AEZs, with 88% of the AEZs showing moderate variability and three AEZs exhibiting high variability in GSL (Fig. 5 c). 3.3. Correlation between rainfall timing and seasonality in maize-based systems The correlation analysis reveals significant variations in rainfall timing and seasonality among maize-based AEZs in Ethiopia (Fig. 6 a–c). The correlation between ROD and RCD ranges from − 0.1 (THMH) to 0.47 (SAMH) (Fig. 6 a). The positive correlation in most AEZs suggests that delayed rainfall onset associated with the delayed rainfall cessation. The correlation between ROD and GSL ranges from − 0.48 to 0.05, indicates that delayed rainfall onset contributed to short GSL in MSHL (-0.48), THMH (-0.34), and SAL (-0.25) AEZs (Fig. 6 b). The strong correlation between RCD and GSL indicates that delayed RCD contributes to longer GSL (Fig. 6 c). 4. Discussion 4.1. Rainfall timing and seasonality The early ROD in Ethiopia signals the beginning of the rainy season, allowing farmers to prepare land and sow crops. Delayed ROD is particularly common in the northern regions leads to reduced crop yields and increased water scarcity. Ethiopia's rainy season follows a gradient, with the earliest ROD in the south/southeast and latest in the west/northwest, spanning from February 19 to August 1. A study conducted primarily in the MH AEZ confirmed that ROD ranges between May 18 and June 8, which over a month later than the present findings (April 05) (Ademe et al., 2021 ). This south-to-north progression is influenced by topography and the Inter-Tropical Convergence Zone, which brings moisture-laden winds from the Indian Ocean northwards (Abebe, 2006 ). This pattern, which aligns with previous studies (Lupi Edao et al., 2018 ; Omay et al., 2023 ; Segele and Lamb, 2005 ; Wakjira et al., 2021 ), determines planting and harvesting seasons, significantly impacting maize production and socio-economic development in Ethiopia. Projections indicate that the delayed rainfall onset in Ethiopia's southwest and eastern regions will alter agricultural calendars, impacting farmers' livelihoods and food security. This shift could lead increased vulnerability to drought and reduced yields, posing significant challenges for farmers who rely on maize as a primary source of food and income. Adaptation strategies will be essential to mitigate these impacts. RCD varies from May 7 to July 30 in southeast and northeast parts, affecting crop planting and harvesting windows. The extreme late RCD, occurring in November in the western part of Ethiopia, can delay crop harvesting and increase the risk of crop damage, affecting overall agricultural productivity. The extreme late rainfall cessation can be attributed to the presence of autumn rainfall in those areas (Wakjira et al., 2021 ). Another study in MH AEZ indicating that the median RCD ranges between October 9 and October 29 (Ademe et al., 2021 ). Understanding these variations is crucial for developing effective adaptation strategies. The GSL in Ethiopia ranges from 35 to 288 days, which is partly consistent with previous studies who reported that the mean GSL ranges 38 to 170 days (Shigute et al., 2023 ). Overall, the trend toward shorter GSL in Ethiopia could have significant implications for maize yield and food security, as reduced cultivation time may decrease productivity. Previous studies have confirmed that the GSL increases from the eastern to the western part of the country (Omay et al., 2023 ; Wakjira et al., 2021 ). A study in MH AEZ reported a median GSL of 120 to 150 days (Ademe et al., 2021 ), while another study in the central rift valley (SAL AEZ) reported a GSL ranging 20 and 256 days (Ademe et al., 2020 ). Conversely, a study a study in the MH and SAMH AEZs reported a median GSL of 75 days (Abegaz, 2020 ). Adaptation strategies, such as developing drought-tolerant and early maturing maize varieties, improving irrigation techniques, and adjusting planting times, will be crucial in mitigating these changes. Geographical variations in GSL highlight the importance of understanding local climate and environmental factors while planning agricultural activities. ROD, RCD, and GSL show significant variations among major maize-growing AEZs, affecting agricultural practices. Early rainfall onset in the THMH and SAL AEZs allows for earlier maize planting, leading to longer growing periods and higher yields. In contrast, late onset in the MSHL AEZ may require farmers to adjust planting schedules to avoid water stress and suboptimal crop growth. The projected variability in rainfall timing across different AEZs of Ethiopia compared to the baseline period suggests the need for adjustments in planting times and farming methods. In areas with shorter delays, farmers may benefit from earlier planting, while those facing longer delays might need to consider alternative farming methods or irrigation systems. Traditional reliance on predictable rainfall patterns in Ethiopia’s maize-dominated agricultural zones is being challenged by climate change, making it difficult for farmers to determine the optimal planting time, leading to reduced yield and potential food insecurity. The impact of early or late ROD on maize yields and agricultural practices in different AEZs is significant. For instance, in a highland AEZ with early ROD, farmers can benefit from longer GS and higher yields as they have ample time for crop establishment and growth. This allows them to adopt advanced agricultural practices such as intercropping or precision farming, leading to increased productivity and income. Conversely, in a lowland AEZ with late ROD, farmers face shorter rowing seasons and higher drought risks resulting in lower maize yields and limited agricultural options. Understanding these variations helps farmers make informed decisions about planting times and variety choices (Cui and Xie, 2022 ). Estimating the median ROD at AEZ level aids in farm planning, identifying drought prone areas, and informing decision on maize variety choice. Improved water management practices and the use of drought-resistant varieties can mitigate these effects. Variations in RCDs also significantly impact maize farmers in Ethiopia. Farmers in SAMH AEZ must carefully select planting and harvesting times to avoid crop losses, while those reside in MSHL zone, with longer growing periods, face risks from late-season rainfall. Understanding RCD variability is crucial for developing tailored agricultural strategies to mitigate the impacts of climate change on maize production. GSL shows high spatial variability across AEZ in Ethiopia, significantly influencing agricultural practices and crop yields. Farmers in SAMH AEZ face a shorter growing season, requiring shorter-maturing varieties, while those in the THMH AEZ can grow long-maturing varieties and achieve higher yields. 4.2. Implications for maize dominated farming systems Climate change is expected to delay ROD, advance RCD, and shorten growing seasons, leading to seasonal water deficit that could severely hamper maize production, especially in AEZs with already short growing seasons in the baseline period (Ademe et al., 2020 ). For instance, regions traditionally experiencing heavy monsoon rains starting in June may see rainfall delayed until July or later, forcing farmers to delay planting, reducing crop yields. Previous studies have shown that that early ROD is negatively correlated with cereal yield, suggesting the potential for higher yields during early rainfall onset (Kumi et al., 2023 ). This effect is particularly pronounced in areas receiving unimodal rainfall (Wakjira et al., 2021 ). The shift in rainfall patterns necessities adjustments to cropping calendars (Wang et al., 2022 ) and effective communication of these changes to facilitate appropriate adaptation strategies. Additionally, climate change may lead to an earlier rainfall cessation, exacerbating water scarcity and causing terminal moisture stresses, further threatening agricultural productivity and food security. Delayed onset and early cessation of rainfall lead to shorter planting windows, reduced maize coverage, and increased risk of crop failure (Iizumi and Ramankutty, 2015 ; Shah et al., 2021 ). These changes underscore the urgent need for adaptation strategies and sustainable farming practices to ensure future food production in a changing climate. 4.3. Possible adaptation options Potential interventions are identified from literatures and presented in Table 2 . Adaptation options include adjusting planting times, utilizing weather forecasting services, implementing water conservation strategies, and switching to short-maturing varieties in regions where rainfall begins late and ends early, narrowing the growing season. Literature suggests that optimizing planting times is a key adaptation strategy in AEZs experiencing significant delays in rainfall onset (Carr et al., 2022 ; Huang et al., 2020 ). Aligning planting schedules with changing rainfall patterns can maximize water use efficiency and improve crop yields. Changing planting window can also mitigate the risk of crop failure due to prolonged dry spells or erratic rainfall patterns. Integrated soil fertility management enhances farming system resilience and climate change adaptation (Arumugam et al., 2023 ; Mubiru et al., 2015 ). Techniques such as organic farming and crop rotation improve soil moisture and nutrient retention, making it more resilient to droughts and extreme weather events. In regions with limited rainfall and shortened growing seasons, moisture conservation practices such as efficient irrigation (Kassie et al., 2015 ) or rainwater harvesting (Mubiru et al., 2015 ) can reduce water loss and ensure a consistent water supply for crops. Although these methods may involve high upfront costs and maintenance, they offer long-term benefits such as cost savings and increased crop productivity. Conservation practices like mulching (Mubiru et al., 2015 ) and cover cropping (Kaye and Quemada, 2017 ) help retain soil moisture and reduce evaporation, and improving water infiltration, enhancing water efficiency and sustaining crop production in the face of water scarcity. Seasonal weather forecasting services and early warning systems are essential for climate change adaptation, enabling farmers to prepare for the upcoming rainy season (Balehegn et al., 2019 ; Coughlan de Perez et al., 2022; Lala et al., 2021 ). Accurate information supports informed decision-making, optimizing planting times and variety choices, and reducing vulnerability to climate-related risks (Agyekum et al., 2022 ). Table 2 Risks to rainfall timing, hotspot AEZs, suggested adaptation options and their roles in major maize growing AEZs of Ethiopia and Rwanda Climate risks Hotspot AEZs Suggested adaptation option The role of the option References Late rainfall onset All AEZs, especially THMH, SAL, MH and WSML Optimizing planting time • Synchronize plating date with ROD • Minimize crop failure and yield reduction Carr et al. ( 2022 ), Huang et al., ( 2020 ), Kassie et al. ( 2013 ), Mugiyo et al. ( 2021 ) Seasonal weather forecasting • Help to make informed decision (when and what to plant) • Reduce crop failure and farming systems vulnerability Agyekum et al. ( 2022 ), Balehegn et al. ( 2019 ), Coughlan de Perez et al. (2022), Lala et al. ( 2021 ), Guido et al. ( 2020 ) Climate risk insurance • Reduced vulnerability by providing financial compensation for production losses • Buffer the financial implications of unexpected crop failure due to late rainfall onset (Falco et al., 2014 ; OECD, 2023 ) (World Bank, 2009 ) Early rainfall cessation All except SAL and THMH Choice of early maturing variety • Complete growth in short period Atiah et al. ( 2021 ), Krell et al. ( 2021 )(Uwiragiye, 2016 ) Rainwater harvesting and supplementary irrigation • Support water supply during tasseling and grain filling stage Assefa et al. ( 2016 ), Ayele ( 2014 ), Gadédjisso-Tossou et al. ( 2018 ) Shrinkage of growing season length All with severe shrinkage in WSML and SAL AEZs Switch to medium to early maturing variety • Help to complete its growth with in short time • Minimize total crop failure or severely reduced yield (Id et al., 2023 ) (Uwiragiye, 2016 ) Climate smart agriculture • Improve the resilience and adaptive capacity of the system • Improve yield and farm income (Olayide et al., 2016 ) Conservation agriculture • Improve the resilience of the system to rainfall variability • Improve soil moisture conditions • Reduce soil loss from 35.5 to 14.5 t/ha/year, 50–70% greater infiltration • Increase 42% of organic carbon, and • Increase yield from 3.6 to 4.4t/ha Milder et al. ( 2012 ), Sime et al. ( 2015 ) (Kabirigi et al., 2015 ) 5. Conclusion Rainfed subsistence agriculture is crucial in East Africa, where crop success heavily depends on rainfall patterns. This study analyzed ROD, RCD, and GSL across major maize-growing AEZs in Ethiopia, using high-resolution historical data (1981–2019) and future projections (2020–2049) from CMIP6 under a strong forcing scenario. Results show significant spatial and temporal variation in ROD, RCD, and GSL. Projections indicate a delayed onset, earlier cessation, and shorter growing seasons, with high interannual variability; signs of increased climate-related risks. Currently, the rainy season typically begins in May/June and ends in September. Projected shifts may expose maize crops to water stress during critical growth stages, threatening yields. These changes underscore the urgency of revising planting calendars, selecting climate-resilient varieties, and exploring additional adaptation strategies. To sustain maize production, adaptation must be tailored to each AEZ. Key strategies include adjusting planting dates, adopting suitable maize varieties, improving irrigation, providing seasonal forecasts, climate risk insurance, and promoting climate-smart practices. Seasonal forecasting will become increasingly important as rainfall timing grows more unpredictable. Strong collaboration between policymakers and local communities is essential for developing and scaling site-specific adaptation measures. Prioritizing support for farmers in regions facing the greatest delays in rainfall onset is critical to enhancing resilience and ensuring regional food security. Declarations Authors’ contributions DA conceived and designed the study, compiled the datasets, performed the analysis, and drafted the manuscript. KT contributed to study design, supported data interpretation, secure funds, supervision, and reviewed the manuscript. BS contributed to the conceptual framework, interpretation of results, supervision, and manuscript revision. AT supported the analysis approach and contributed to manuscript editing. TG contributed to data acquisition/processing and interpretation of results. FG supported data compilation and quality control and contributed to manuscript revision. AN contributed to interpretation of findings and critical revision of the manuscript. Availability of data and material/Data availability The datasets analyzed in this study are publicly available. Any processed data files generated during the current study are available from the corresponding author upon reasonable request. Code availability The code used for data processing and analysis is available from the corresponding author upon reasonable request. Funding This research was funded by the Alliance for a Green Revolution in Africa (AGRA). Conflicts of interest/Competing interests The authors declare that they have no competing interests. Consent for publication All authors have read and approved the final manuscript and agree to its publication. Ethics approval/declarations Not applicable. Consent to participate Not applicable. Acknowledgment Authors would like to acknowledge Alliance for Green Revolution Africa (AGRA) for the financial support. We would also like to thank CIMMYT for providing all the necessary facilities require for the study. References Abate, T., Shiferaw, B., Menkir, A., Wegary, D., Kebede, Y., Tesfaye, K., Kassie, M., Bogale, G., Tadesse, B., Keno, T., 2015. Factors that transformed maize productivity in Ethiopia. Food Secur. 7, 965–981. https://doi.org/10.1007/s12571-015-0488-z Abebe, M., 2006. The Onset , Cessation and Dry Spells of the Small Rainy Season ( Belg ) of Ethiopia. Abegaz, W.B., 2020. Rainfall Variability and Trends over Central Ethiopia. Int. J. Environ. Sci. Nat. Resour. 24. https://doi.org/10.19080/ijesnr.2020.24.556144 Ademe, D., Zaitchik, B.F., Tesfaye, K., Simane, B., Alemayehu, G., Adgo, E., 2021. Analysis of agriculturally relevant rainfall characteristics in a tropical highland region: An agroecosystem perspective. Agric. For. Meteorol. 311, 1–12. https://doi.org/10.1016/j.agrformet.2021.108697 Ademe, D., Ziatchik, B.F., Tesfaye, K., Simane, B., Alemayehu, G., Adgo, E., 2020. Climate trends and variability at adaptation scale: Patterns and perceptions in an agricultural region of the Ethiopian Highlands. Weather Clim. Extrem. 29, 100263. https://doi.org/10.1016/j.wace.2020.100263 Ademe, F., Kibret, K., Beyene, S., Mitike, G., Getinet, M., 2020. Rainfall analysis for rain-fed farming in the great rift valley basins of Ethiopia. J. Water Clim. Chang. 11, 812–828. https://doi.org/10.2166/wcc.2019.242 Agyekum, T.P., Antwi-Agyei, P., Dougill, A.J., 2022. The contribution of weather forecast information to agriculture, water, and energy sectors in East and West Africa: A systematic review. Front. Environ. Sci. 10, 1–14. https://doi.org/10.3389/fenvs.2022.935696 Akinseye, F.M., Agele, S.O., Traore, P.C.S., Adam, M., Whitbread, A.M., 2016. Evaluation of the onset and length of growing season to define planting date—‘a case study for Mali (West Africa).’ Theor. Appl. Climatol. 124, 973–983. https://doi.org/10.1007/s00704-015-1460-8 Allan, R.P., Barlow, M., Byrne, M.P., Cherchi, A., Douville, H., Fowler, H.J., Gan, T.Y., Pendergrass, A.G., Rosenfeld, D., Swann, A.L.S., Wilcox, L.J., Zolina, O., 2020. Advances in understanding large-scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci. 1472, 49–75. https://doi.org/10.1111/nyas.14337 Amarasingha, R.P.R.K., Galagedara, L.W., Marambe, B., Silva, G.L.L.P., Punyawardena, R., Nidumolu, U., Howden, M., Suriyagoda, L.D.B., 2015. Aligning Sowing Dates with the Onset of Rains to Improve Rice Yields and Water Productivity: Modelling Rice (Oryza sativa L.) Yield of the Maha Season in the Dry Zone of Sri Lanka. Trop. Agric. Res. 25, 277. https://doi.org/10.4038/tar.v25i3.8038 Amekudzi, L.K., Yamba, E.I., Preko, K., Asare, E.O., Aryee, J., Baidu, M., Codjoe, S., 2015. Variabilities in Rainfall Onset, Cessation and Length of Rainy Season for the Various Agro-Ecological Zones of Ghana. Climate 3, 416–434. https://doi.org/10.3390/cli3020416 Araya, A., Hoogenboom, G., Luedeling, E., Hadgu, K.M., Kisekka, I., Martorano, L.G., 2015. Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia. Agric. For. Meteorol. 214–215, 252–265. https://doi.org/10.1016/j.agrformet.2015.08.259 Arnell, N.W., 2004. Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob. Environ. Chang. 14, 31–52. https://doi.org/10.1016/j.gloenvcha.2003.10.006 Arumugam, P., Chemura, A., Aschenbrenner, P., Schauberger, B., Gornott, C., 2023. Climate change impacts and adaptation strategies: An assessment on sorghum for Burkina Faso. Eur. J. Agron. 142. https://doi.org/10.1016/j.eja.2022.126655 Assefa, S., Biazin, B., Muluneh, A., Yimer, F., Haileslassie, A., 2016. Rainwater harvesting for supplemental irrigation of onions in the southern dry lands of Ethiopia. Agric. Water Manag. 178, 325–334. https://doi.org/10.1016/j.agwat.2016.10.012 Atiah, W.A., Muthoni, F.K., Kotu, B., Kizito, F., Amekudzi, L.K., 2021. Trends of rainfall onset, cessation, and length of growing season in northern ghana: Comparing the rain gauge, satellite, and farmer’s perceptions. Atmosphere (Basel). 12. https://doi.org/10.3390/atmos12121674 Ayele, Y.A., 2014. Rainwater Harvesting for Climate Change Adaptation in Ethiopia: Policy and Institutional Analysis. Balehegn, M., Balehey, S., Fu, C., Liang, W., 2019. Indigenous weather and climate forecasting knowledge among Afar pastoralists of north eastern Ethiopia: Role in adaptation to weather and climate variability. Pastoralism 9. https://doi.org/10.1186/s13570-019-0143-y Baptista, D., Farid, M., 2022. Climate Change and Chronic Food Insecurity in Sub-Saharan Africa. Basu, S., Ramegowda, V., Kumar, A., Pereira, A., 2016. Plant adaptation to drought stress. F Res. 5, 1–10. https://doi.org/10.12688/F1000RESEARCH.7678.1 Birhan, D.A., Zaitchik, B.F., Fantaye, K.T., Birhanu, B.S., Damot, G.A., Tsegaye, E.A., 2022. Observed and projected trends in climate extremes in a tropical highland region: An agroecosystem perspective. Int. J. Climatol. 42, 2493–2513. https://doi.org/10.1002/joc.7378 Bucagu, C., Ndoli, A., Cyamweshi, A.R., Nabahungu, L.N., Mukuralinda, A., Smethurst, P., 2020. Determining and managing maize yield gaps in Rwanda. Food Secur. 12, 1269–1282. https://doi.org/10.1007/s12571-020-01059-2 Cairns, J.E., Hellin, J., Sonder, K., Araus, J.L., MacRobert, J.F., Thierfelder, C., Prasanna, B.M., 2013. Adapting maize production to climate change in sub-Saharan Africa. Food Secur. 5, 345–360. https://doi.org/10.1007/s12571-013-0256-x Cannon, A.J., Sobie, S.R., Murdock, T.Q., 2015. Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1 Carr, T.W., Mkuhlani, S., Segnon, A.C., Ali, Z., Zougmoré, R., Dangour, A.D., Green, R., Scheelbeek, P., 2022. Climate change impacts and adaptation strategies for crops in West Africa: A systematic review. Environ. Res. Lett. 17. https://doi.org/10.1088/1748-9326/ac61c8 Chawarska, K., Klin, A., Paul, R., Macari, S., Volkmar, F., 2009. A prospective study of toddlers with ASD: Short-term diagnostic and cognitive outcomes. J. Child Psychol. Psychiatry Allied Discip. 50, 1235–1245. https://doi.org/10.1111/j.1469-7610.2009.02101.x Chrysostom, N.J., 2014. Analysis of economic efficiency of maize production in Huye district in Rwanda. Int. J. Agric. Innov. Res. 3, 707–716. Claude, R., 2017. Maize in Rwanda : A Value Chain Analysis. Univ. Nairobi 1, 9. Context Network, 2016. Rwanda Early Generation Seed Study: Country Report 1–91. Coughlan de Perez, E., Harrison, L., Berse, K., Easton-Calabria, E., Marunye, J., Marake, M., Murshed, S.B., Shampa, Zauisomue, E.H., 2022. Adapting to climate change through anticipatory action: The potential use of weather-based early warnings. Weather Clim. Extrem. 38, 100508. https://doi.org/10.1016/j.wace.2022.100508 CSA, 2022. AGRICULTURAL SAMPLE SURVEY AREA AND PRODUCTION OF MAJOR CROPS. CSA, 2018. The Federal Democratic Republic of Ethiopia Centeral Statistical Agency Agricultural Sample Survey 2017/18 (2010 E.C.): Report on area and production for major crops (private peasant holdings, Meher season), Statistical Bulletin No. 586. CSA, 2016. Agricultural sample surveys:2015/2016, Statistical Bulletin No. 584. https://doi.org/10.1038/155183a0 CSA, 2015. Agricultural Sample Survey Time Series Data for National & Regional Level (1995/96-2014/15). Cui, X., Xie, W., 2022. Adapting Agriculture to Climate Change through Growing Season Adjustments: Evidence from Corn in China. Am. J. Agric. Econ. 104, 249–272. https://doi.org/10.1111/ajae.12227 Degebasa, L., Tessema, T., Bekeko, Z., Belete, K., 2022. Prevalence and Socioeconomic Impact of Striga (Striga hermonthica) in Sorghum Producing Areas of East and West Hararghe Zones, Oromia, Ethiopia. Int. J. Agron. 2022. https://doi.org/10.1155/2022/8402280 Demissie, G., Deressa, T., Haile, M., Bogale, G., Dida, M., Chibsa, T., Gedefaw, Y., Tulu, D., Debelo, M., Keno, T., Azmach, and G., 2016. Prevalence, Distribution and Impact of Maize Lethal Necrosis Disease (MLND) in Ethiopia. Pest Manag. J. Ethiop. 18, 37–49. Dunning, C.M., Black, E.C.L., Allan, R.P., 2016. The onset and cessation of seasonal rainfall over Africa. J. Geophys. Res. 121, 11405–11424. https://doi.org/10.1002/2016JD025428 Eggen, M., Ozdogan, M., Zaitchik, B., Ademe, D., Foltz, J., Simane, B., 2019. Vulnerability of sorghum production to extreme, sub-seasonal weather under climate change. Environ. Res. Lett. 14, 045005. https://doi.org/10.1088/1748-9326/aafe19 Falco, S. Di, Adinolfi, F., Bozzola, M., Capitanio, F., 2014. Crop Insurance as a Strategy for Adapting to Climate Change. J. Agric. Econ. 65, 485–504. https://doi.org/10.1111/1477-9552.12053 FAO, 2019. GIEWS Country Brief Zambia, Glogbal Watch GIEWS. Feleke, H.G., Savage, M.J., Fantaye, K.T., Rettie, F.M., 2023. The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia. Atmosphere (Basel). 14, 497. https://doi.org/10.3390/atmos14030497 Friesen, D. k., Palmer, A.F.E., 2002. Integrated Approaches to Higher Maize Productivity in the New Millennium, In Friesen DK and Palmer AFE (2002) Proceedings of the Seventh Eastern and Southern Africa Regional Maize Conference Nairobi, Kenya 5 – 11 February 2002. Funk, C., Harrison, L., Alexander, L., Peterson, P., Behrangi, A., Husak, G., 2019a. Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions. Environ. Res. Lett. 14. https://doi.org/10.1088/1748-9326/ab4a6c Funk, C., Peterson, P., Peterson, S., Shukla, S., Davenport, F., Michaelsen, J., Knapp, K.R., Landsfeld, M., Husak, G., Harrison, L., Rowland, J., Budde, M., Meiburg, A., Dinku, T., Pedreros, D., Mata, N., 2019b. A high-resolution 1983–2016 TMAX climate data record based on infrared temperatures and stations by the climate hazard center. J. Clim. 32, 5639–5658. https://doi.org/10.1175/JCLI-D-18-0698.1 Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., Verdin, A.P., 2014. A Quasi-Global Precipitation Time Series for Drought Monitoring. U.S. Geol. Surv. Data Ser. 832, 4. Gadédjisso-Tossou, A., Avellán, T., Schütze, N., 2018. Potential of deficit and supplemental irrigation under climate variability in northern Togo, West Africa. Water (Switzerland) 10. https://doi.org/10.3390/w10121803 Gbangou, T., Ludwig, F., van Slobbe, E., Hoang, L., Gordana Kranjac-Berisavljevic, Slobbe, E. Van, Hoang, L., 2019. Seasonal variability and predictability of agro-meteorological indices: Tailoring onset of rainy season estimation to meet farmers’ needs in Ghana. Clim. Serv. 14, 19–30. https://doi.org/10.1016/j.cliser.2019.04.002 Gezahegn, B., Gebire, W., Bamud, K., 2018. Identification of major crop production constraints in debub ari district, south omo zone, southern Ethiopia. Hortic. Int. J. 2, 383–388. https://doi.org/10.15406/hij.2018.02.00081 Ginbo, T., 2022. Heterogeneous impacts of climate change on crop yields across altitudes in Ethiopia. Clim. Change 170. https://doi.org/10.1007/s10584-022-03306-1 Guido, Z., Zimmer, A., Lopus, S., Hannah, C., Gower, D., Waldman, K., Krell, N., Sheffield, J., Caylor, K., Evans, T., 2020. Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa. Clim. Risk Manag. 30, 100247. https://doi.org/10.1016/j.crm.2020.100247 Hempel, S., Frieler, K., Warszawski, L., Schewe, J., Piontek, F., 2013. A trend-preserving bias correction – The ISI-MIP approach. Earth Syst. Dyn. 4, 219–236. https://doi.org/10.5194/esd-4-219-2013 Herve, M., 2019. Investigating the Effect of Climate Variability and Change on Maize Yield in Rwanda. University of Nirobi. Huang, M., Wang, J., Wang, B., Liu, D.L., Yu, Q., He, D., Wang, N., Pan, X., 2020. Optimizing sowing window and cultivar choice can boost China’s maize yield under 1.5 °C and 2 °C global warming. Environ. Res. Lett. 15. https://doi.org/10.1088/1748-9326/ab66ca Hunter, R., Crespo, O., Coldrey, K., Cronin, K., New, M., 2020. Climate Change and Future Crop Suitability in Rwanda. Cape Town. Id, D.M., Id, W.W., Mamo, G., 2023. Spatio-temporal variability and rainfall trend affects seasonal calendar of maize production in southern central Rift Valley of Ethiopia. PLOS Clim. 1–31. https://doi.org/10.1371/journal.pclm.0000218 Iizumi, T., Ramankutty, N., 2015. How do weather and climate influence cropping area and intensity? Glob. Food Sec. 4, 46–50. https://doi.org/10.1016/j.gfs.2014.11.003 Kabirigi, M., Musana, B., Ngetich, F., Mugwe, J., Mukuralinda, A., Nabahungu, N.L., 2015. Applicability of conservation agriculture for climate change adaptation in Rwanda’s situation. J. soil Sci. Environ. Manag. 6, 241–248. https://doi.org/10.5897/JSSEM15.0508 Kassie, B.T., Asseng, S., Rotter, R.P., Hengsdijk, H., Ruane, A.C., Van Ittersum, M.K., 2015. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145–158. https://doi.org/10.1007/s10584-014-1322-x Kassie, B.T., Hengsdijk, H., Rötter, R., Kahiluoto, H., Asseng, S., Van Ittersum, M., 2013. Adapting to climate variability and change: Experiences from cereal-based farming in the central rift and kobo valleys, Ethiopia. Environ. Manage. 52, 1115–1131. https://doi.org/10.1007/s00267-013-0145-2 Kassie, B.T., Van Ittersum, M.K., Hengsdijk, H., Asseng, S., Wolf, J., Rötter, R.P., 2014. Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia. F. Crop. Res. 160, 41–53. https://doi.org/10.1016/j.fcr.2014.02.010 Kaye, J.P., Quemada, M., 2017. Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev. 37. https://doi.org/10.1007/s13593-016-0410-x Kindie Tesfaye, Sika Gbegbelegbe, Jill E. Cairns, Bekele Shiferaw, Boddupalli M. Prasanna, Kai Sonder, Ken Boote, Dan Makumbi, R.R., 2015. Maize systems under climate change in sub-Saharan Africa Potential impacts on production and food security. Int. J. Clim. Chang. Strateg. Manag. 7. https://doi.org/10.1108/17568690910955603 Krell, N.T., Morgan, B.E., Gower, D., Caylor, K.K., 2021. Consequences of Dryland Maize Planting Decisions Under Increased Seasonal Rainfall Variability. Water Resour. Res. 57, 1–28. https://doi.org/10.1029/2020WR029362 Kumi, N., Adeliyi, T.E., Asante, V.A., Abiodun, B.J., Lamptey, B.L., 2023. Impact of rainfall onset date on crops yield in Ghana. Front. Sustain. Food Syst. 7, 1–10. https://doi.org/10.3389/fsufs.2023.1176385 Lala, J., Yang, M., Wang, G., Block, P., 2021. Utilizing rainy season onset predictions to enhance maize yields in Ethiopia. Environ. Res. Lett. 16. https://doi.org/10.1088/1748-9326/abf9c9 Levy, P.E., Cannell, M.G.R., Friend, A.D., 2004. Modelling the impact of future changes in climate, CO2 concentration and land use on natural ecosystems and the terrestrial carbon sink. Glob. Environ. Chang. 14, 21–30. https://doi.org/10.1016/j.gloenvcha.2003.10.005 Lupi Edao, A., Kibert, K., Mamo, G., 2018. Analysis of Start, End and Length of the Growing Season and Number of Rainy Days in Semi-Arid Central Refit Valley of Oromia State, Ethiopia. Adv. Crop Sci. Technol. 06. https://doi.org/10.4172/2329-8863.1000386 Marteau, R., Sultan, B., Moron, V., Alhassane, A., Baron, C., Traoré, S.B., 2011. The onset of the rainy season and farmers’ sowing strategy for pearl millet cultivation in Southwest Niger. Agric. For. Meteorol. 151, 1356–1369. https://doi.org/10.1016/j.agrformet.2011.05.018 Mellander, P.E., Gebrehiwot, S.G., Gärdenäs, A.I., Bewket, W., Bishop, K., 2013. Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns. PLoS One 8, 1–12. https://doi.org/10.1371/journal.pone.0068461 Merga, T., Zhang, W., Shuaixiang, Z., Zhiping, D., 2022. Enhancing maize yield in Ethiopia a. Int. J. Agric. Sci. Food Technol. 8, 193–201. Milder, J.C., Majanen, T., Scherr, S.J., 2012. Performance and Potential of Conservation Agriculture for Climate Change Adaptation and Mitigation in Sub-Saharan Africa An assessment of WWF and CARE projects in support of the WWF-CARE Alliance’s Rural Futures Initiative. Mohammed, M., 2021. Empirical Analysis of the Factors that Affecting Maize Production of Farmers among Smallholders: The Case of Eastern Oromia, Ethiopia. Turkish J. Agric. - Food Sci. Technol. 9, 137–145. https://doi.org/10.24925/turjaf.v9i1.137-145.3769 Mosisa Worku, Hussein Shimelis, A., Laing, M., 2013. Preferences and constraints of maize farmers in the development and adoption of improved varieties in the mid-altitude, sub-humid agro-ecology of Western Ethiopia. African J. Agric. Res. 8, 1245–1254. https://doi.org/10.5897/ajar12.1972 Mubiru, D.N., Kyazze, F.B., Radeny, M., Zziwa, A., Lwasa, J., Kinyangi, J., 2015. Climatic trends, risk perceptions and coping strategies of smallholder farmers in rural Uganda. CCAFS Work. Pap. 41 pp. Mugalavai, E.M., Kipkorir, E.C., Raes, D., Rao, M.S., 2008. Analysis of rainfall onset , cessation and length of growing season for western Kenya 148, 1123–1135. https://doi.org/10.1016/j.agrformet.2008.02.013 Mugiyo, H., Mhizha, T., Chimonyo, V.G.P., Mabhaudhi, T., 2021. Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe. Heliyon 7, e06109. https://doi.org/10.1016/j.heliyon.2021.e06109 Mulungu, K., N. Ng’ombe, J., 2020. Climate Change Impacts on Sustainable Maize Production in Sub-Saharan Africa: A Review. Maize - Prod. Use 1–11. https://doi.org/10.5772/intechopen.90033 National Institute of Statstics of Rwanda, 2019. Seasonal Agricultural Survey 2019. Ngango, J., Hong, S., 2021. Improving farm productivity through the reduction of managerial and technology gaps among farmers in Rwanda. Agric. Food Secur. 10, 1–14. https://doi.org/10.1186/s40066-020-00284-4 OECD, 2023. Enhancing the insurance sector’s contribution to climate adaptation. Olayide, O.E., Tetteh, I.K., Popoola, L., 2016. Differential impacts of rainfall and irrigation on agricultural production in Nigeria: Any lessons for climate-smart agriculture? Agric. Water Manag. 178, 30–36. https://doi.org/10.1016/j.agwat.2016.08.034 Omay, P.O., Muthama, N.J., Oludhe, C., Kinama, J.M., Artan, G., Atheru, Z., 2023. Changes and variability in rainfall onset, cessation, and length of rainy season in the IGAD region of Eastern Africa. Theor. Appl. Climatol. 152, 871–893. https://doi.org/10.1007/s00704-023-04433-0 Raziei, T., Pereira, L.S., 2013. Estimation of ETo with Hargreaves-Samani and FAO-PM temperature methods for a wide range of climates in Iran. Agric. Water Manag. 121, 1–18. https://doi.org/10.1016/j.agwat.2012.12.019 Report, A. project progress, 2021. Increasing maize value chain efficiency in Rwanda. Rockström, J., Karlberg, L., Wani, S.P., Barron, J., Hatibu, N., Oweis, T., Bruggeman, A., Farahani, J., Qiang, Z., 2010. Managing water in rainfed agriculture-The need for a paradigm shift. Agric. Water Manag. 97, 543–550. https://doi.org/10.1016/j.agwat.2009.09.009 Rwanda Meteorology Agency, 2022. Season A 2022 . Issue No . 01 January 2022 Rwanda National Crop Monitor Season A 2022 . Issue No . 01 January 2022 Rwanda National Crop Monitor. Santiago Begueríae, S.M.V.-S., 2023. Package ‘ SPEI ’: Calculation of the Standardized Precipitation-Evapotranspiration Index. Sarr, B., 2012. Present and future climate change in the semi-arid region of West Africa : a crucial input for practical adaptation in. Atmos. Sci. Lett. 13, 108–112. https://doi.org/10.1002/asl.368 Segele, Z.T., Lamb, P.J., 2005. Characterization and variability of Kiremt rainy season over Ethiopia. Meteorol. Atmos. Phys. 89, 153–180. https://doi.org/10.1007/s00703-005-0127-x Selvaraju, R., 2011. Climate risk assessment and management in agriculture. Build. Resil. Adapt. to Clim. Chang. Agric. Sect. 23, 71–90. https://doi.org/doi:10.1103/PhysRevD.64.104020 Senapathy, M., 2021. Evaluation of Maize (Zea mays L.) Varieties for Moisture Stress Areas in Humbo District, Wolaita Zone, Southern Ethiopia. Int. J. Agric. Environ. Biotechnol. 14, 75–82. https://doi.org/10.30954/0974-1712.01.2021.8 Shah, H., Hellegers, P., Siderius, C., 2021. Climate risk to agriculture: A synthesis to define different types of critical moments. Clim. Risk Manag. 34, 100378. https://doi.org/10.1016/j.crm.2021.100378 Shigute, M., Alamirew, T., Abebe, A., Ndehedehe, C.E., Kassahun, H.T., 2023. Analysis of rainfall and temperature variability for agricultural water management in the upper Genale river basin, Ethiopia. Sci. African 20. https://doi.org/10.1016/j.sciaf.2023.e01635 Shukla, S., Husak, G., Turner, W., Davenport, F., Funk, C., Harrison, L., Krell, N., 2021. A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa. PLoS One 16, 1–21. https://doi.org/10.1371/journal.pone.0242883 Sime, G., Aune, J.B., Mohammed, H., 2015. Agronomic and economic response of tillage and water conservation management in maize, central rift valley in Ethiopia. Soil Tillage Res. 148, 20–30. https://doi.org/10.1016/j.still.2014.12.001 Stuch, B., Alcamo, J., Schaldach, R., 2021. Projected climate change impacts on mean and year-to-year variability of yield of key smallholder crops in Sub-Saharan Africa. https://doi.org/10.1080/17565529.2020.1760771 Suryabhagavan, K. V., 2017. GIS-based climate variability and drought characterization in Ethiopia over three decades. Weather Clim. Extrem. 15, 11–23. https://doi.org/10.1016/j.wace.2016.11.005 Tesfaye, K., Gbegbelegbe, S., Cairns, J.E., Shiferaw, B., Prasanna, B.M., Sonder, K., Boote, K., Makumbi, D., Robertson, R., 2015. Maize systems under climate change in sub-Saharan Africa: Potential impacts on production and food security. Int. J. Clim. Chang. Strateg. Manag. 7, 247–271. https://doi.org/10.1108/IJCCSM-01-2014-0005 Themeßl, M.J., Gobiet, A., Heinrich, G., 2012. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim. Change 112, 449–468. https://doi.org/10.1007/s10584-011-0224-4 Urgessa, T.B., 2014. Review of challenges and prospects of agricultural production and productivity in Ethiopia. J. Nat. Sci. Res. 4, 70–77. Uwiragiye, A., 2016. Assessing the Impact of Climate Change and Variability on Wetland Maize Production and Food Security in Highlands and Central Plateaus of Rwanda Case Study of Bahimba and Bishenyi Wetlands. UR Libr. 11, 31–48. Viste, E., Korecha, D., Sorteberg, A., 2013. Recent drought and precipitation tendencies in Ethiopia. Theor. Appl. Climatol. 112, 535–551. https://doi.org/10.1007/s00704-012-0746-3 Wainwright, C.M., Black, E., Allan, R.P., 2021. Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 simulations. J. Hydrometeorol. 2339–2357. https://doi.org/10.1175/jhm-d-21-0017.1 Wakjira, M.T., Peleg, N., Anghileri, D., Molnar, D., Alamirew, T., Six, J., Molnar, P., 2021. Rainfall seasonality and timing: implications for cereal crop production in Ethiopia. Agric. For. Meteorol. 310, 108633. https://doi.org/10.1016/j.agrformet.2021.108633 Wang, X., Folberth, C., Skalsky, R., Wang, S., Chen, B., Liu, Y., Chen, J., Balkovic, J., 2022. Crop calendar optimization for climate change adaptation in rice-based multiple cropping systems of India and Bangladesh. Agric. For. Meteorol. 315, 108830. https://doi.org/10.1016/j.agrformet.2022.108830 WFP, 2021. Evaluation Brief. World Bank, 2009. Making Development Climate Resilient. A World Bank Strategy for Sub-Saharan Africa, Report No. 46947-AFR. Additional Declarations No competing interests reported. Supplementary Files AppendixTable1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8921665","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603020099,"identity":"d64a19a7-6a05-495e-b295-7500400e7e22","order_by":0,"name":"Dereje Birhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACA2YGNhDN2MDewPDhAYhBvBaeA4wzEojSwgDTIpFApBZzdt5nD37uYJDtn/nGsCGBwUZ2wwECWiyb2c0Ne88wGM+4nQPSkmZMUIvBYTY2Cd42hsSG2znmDxIYDicSpUXyL1DL/JtnQLb8J06LNMiWDTd4QFoOENZi2QzUItsmYbzxTFphQ4JBsvFMQlrM+Y+xSb5ts5Gdd/zwxoYPFXayfYS0QIEEzJ3EKR8Fo2AUjIJRQAAAAFDTQsin1l9SAAAAAElFTkSuQmCC","orcid":"","institution":"Debre Markos University","correspondingAuthor":true,"prefix":"","firstName":"Dereje","middleName":"","lastName":"Birhan","suffix":""},{"id":603020101,"identity":"a56810d3-2440-4450-85c8-63e47b9781d8","order_by":1,"name":"Kindie Tesfaye","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Kindie","middleName":"","lastName":"Tesfaye","suffix":""},{"id":603020102,"identity":"235c9dd3-3ca8-4585-a634-259c01298c06","order_by":2,"name":"Belay Simane","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Belay","middleName":"","lastName":"Simane","suffix":""},{"id":603020106,"identity":"c120d1c7-86f5-4fd4-8c68-a72c1a806ec4","order_by":3,"name":"Alemu Tolemariam","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Alemu","middleName":"","lastName":"Tolemariam","suffix":""},{"id":603020107,"identity":"963214b3-5fb9-4a77-97c4-1e4efee64000","order_by":4,"name":"Temesgen Gebremariam","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Temesgen","middleName":"","lastName":"Gebremariam","suffix":""},{"id":603020108,"identity":"ea36eb6e-0b74-42b3-974b-a0a2b13a6f39","order_by":5,"name":"ADAMA NDOUR","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"ADAMA","middleName":"","lastName":"NDOUR","suffix":""},{"id":603020109,"identity":"9b4087b6-3988-44ac-9d5b-660d7003d0c3","order_by":6,"name":"Fite Getaneh","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Fite","middleName":"","lastName":"Getaneh","suffix":""}],"badges":[],"createdAt":"2026-02-20 02:08:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8921665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8921665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104920874,"identity":"7e14c886-56bc-456a-8f92-a2a17887445e","added_by":"auto","created_at":"2026-03-18 17:30:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314944,"visible":true,"origin":"","legend":"\u003cp\u003eMaize-based major agroecological zones and associated farming systems in Ethiopia (adapted from \u003ca href=\"https://gaez.fao.org/\"\u003eGAEZ v4 Data Portal (fao.org)\u003c/a\u003e); SAL= Semi-arid lowland, WSML= Warm sub-mist lowland, MSHL=Moist to sub-humid lowland, SAMH= Semi-arid moist highland, MH= Moist highland, THMH= Tepid humid moist highland, CH= cool humid, CSH= Cool sub humid.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/266e3fa207564a38efae9b15.png"},{"id":104920879,"identity":"6c282a84-10ad-4c63-bd4f-89da0574d27f","added_by":"auto","created_at":"2026-03-18 17:30:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439739,"visible":true,"origin":"","legend":"\u003cp\u003eObserved (1981-2019) and projected (202-2049) rainfall onset date (a, b, g and h) rainfall cessation date (c, d, i, and j) and growing season length (e, f, k and l) in maize-growing areas of Ethiopia (a-f) and Rwanda (g-l).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/cc958a2452435d4385908540.png"},{"id":104920877,"identity":"05d2cd9c-1d02-4d9f-813d-2e22dd03d02e","added_by":"auto","created_at":"2026-03-18 17:30:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41535,"visible":true,"origin":"","legend":"\u003cp\u003eROD (a), RCD (b) and GSL (c) in major maize growing areas of Ethiopia. Diamond shaped points show the median date and line with bars showed the standard deviation of the events. WSML= Warm sub-moist lowland, MSHL= Moist to sub-humid lowlands, THMH= Tepid humid mid highlands, SAL= Semi-Arid lowlands, MH= Moist highlands, SAMH = Semi-Arid mid-highlands.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/d0c1df808e85121748f60243.png"},{"id":105034386,"identity":"e087c31f-9dbc-4c32-9d42-e2b66823325b","added_by":"auto","created_at":"2026-03-20 07:23:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59445,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in ROD, RCD, and GSL in major maize growing AEZs of Ethiopia\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/a705da3ebe929a51b268e5ac.png"},{"id":105034471,"identity":"1df26829-02cd-469e-a5f9-b4193d00ff50","added_by":"auto","created_at":"2026-03-20 07:23:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67050,"visible":true,"origin":"","legend":"\u003cp\u003eAgroecological zone level interannual variability of ROD (a), RCD (b) and GSL (c) in the baseline and future period in maize-based areas of Ethiopia and Rwanda\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/5096464a721a509b2868a2bf.png"},{"id":104920878,"identity":"e9476d1e-dbca-4fce-b631-a95802595035","added_by":"auto","created_at":"2026-03-18 17:30:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":252406,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between ROD with RCD (a), ROD with GSL (b) and RCD with GSL (c) in maize-based AEZs of Ethiopia\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/93cb841f19f60d04c78e39c3.png"},{"id":105562545,"identity":"8694ebce-e2d9-47b4-846c-1f49030d623a","added_by":"auto","created_at":"2026-03-27 12:42:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2077675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/380703e6-c741-4052-b013-c30578e1c678.pdf"},{"id":105034834,"identity":"883c675e-d83c-4aad-8bdc-96660f065b51","added_by":"auto","created_at":"2026-03-20 07:24:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19299,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8921665/v1/3263b275d19b0c29265d7b30.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate change affects rainfall seasonality and timing in maize-based agroecological zones of Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize (Zea mays L.) is a critical cereal crop and cornerstone of food security in sub-Saharan Africa (Mulungu and N. Ng\u0026rsquo;ombe, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is cultivated across a diverse range of environments, from dry lowlands to highlands (Cairns et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Ethiopia, maize is grown extensively across all regions with mid-altitude areas being the most predominant (Chawarska et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similarly, Rwanda\u0026rsquo;s wet mid-altitude areas are identified as key maize-growing zones.\u003c/p\u003e \u003cp\u003eIn Ethiopia, maize is the most widely cultivated crop, ranking first in productivity and second in area coverage (Mohammed, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its importance to food security surged following the 1984 drought famine (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and today, over 11\u0026nbsp;million smallholder households grow maize in Ethiopia (CSA, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Between 1995 and 2021, the area of maize production expanded significantly from 1.2\u0026nbsp;million hectares to 2.5\u0026nbsp;million hectares, and productivity increased from 1.19 t/ha to 2.63 t/ha (CSA, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This doubling of productivity within two decades is attributed to the increased adoption of improved maize varieties, expanded extension services, and favorable weather conditions in major maize-growing regions (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, despite these gains, maize production remains highly variable across years, with recent years showing greater fluctuations in yield and coverage.\u003c/p\u003e \u003cp\u003eIn Rwanda maize is the third most important crop covering 10% of the total cultivated land. It is predominantly grown by smallholder farmers (WFP, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since 2003, maize production has tripled, driven by rising demand (Chrysostom, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the introduction hybrid maize varieties (Claude, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Context Network, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with the government using seed and fertilizer subsidies to encourage its adoption (Context Network, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Government initiatives, including seed and fertilizer subsidies, have further boosted maize production (Ngango and Hong, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the productivity of maize reaching 1.46 t/ha (National Institute of Statstics of Rwanda, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoisture availability is a key determinant of the maize-growing season in tropical regions. Rwanda experiences two distinct growing seasons: Season A, the main maize-growing season in the country (Agriterra Report, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (September to January) and Season B (February to May), with Season A being the main maize-growing period (FAO, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its increasing coverage and productivity relative to with other cereals, maize yield gaps in Ethiopia remain substantial, largely due to factors such as limited access to capital, small farm sizes, economic constraints, and poor market access (Mohammed, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Low soil fertility, high input costs, and early cessation of rainfall are major production challenges, compounded by disease (Worku et al., 2013), insects pests (Friesen and Palmer, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gezahegn et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), parasitic weeds (Degebasa et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and genetic factors (Senapathy, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Maize lethal necrosis disease, in particular, can cause up to 100% yield in many parts of Ethiopia (Demissie et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change and variability are also major biophysical constraints affecting maize production in Sub-Saharan Africa. The region\u0026rsquo;s vulnerability to climate change is heightened by its dependence on natural resources and rained agriculture both of which are highly sensitive to climate variability (World Bank, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In Rwanda, the 2022 Season A in the cool humid region was characterized by late rains and poor distribution, resulting in low maize sector performance (Rwanda Meteorology Agency, 2022). Projections under RCP8.5 scenario suggest that by 2035, changes in rainfall timing could reduce maize by up to 8% maize yield reduction in season (Herve, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change is expected to reduce maize yields by 5%\u0026ndash;25%, particularly in moist lowland and semi-arid areas of Ethiopia (Ginbo, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tesfaye et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stuch et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, certain agroecological zone (AEZs) such as tepid humid mid-highland, sub-humid mid-highland, moist highland, and per-humid area of Ethiopia may experience maize yield gains 5%\u0026ndash;50% due to favorable climatic conditions (Tesfaye et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stuch et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change is projected to significantly impact maize production in Ethiopia by altering land suitability. Projections indicate that some areas in arid and semi-arid highlands may lose maize-producing capacity by the 2050s (Tesfaye et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, studies suggest a potential 13% reduction in maize GSL by the 2030s, leading to a 3.6% reduction in maize yield (Araya et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). High interannual fluctuations in area coverage and productivity, influenced by rainfall timing and seasonality, could further exacerbate yield reductions (Kassie et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change is intensifying and contributing to food insecurity in the East African region, affecting economic growth and increasing poverty levels (Baptista and Farid, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is largely due to the vulnerability of rainfed crop production systems, which are particularly sensitive to rainfall variability and emerging climate trends (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D. Ademe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Birhan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mellander et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Urgessa, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and limited climate information access (Gbangou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal climate variability negatively impacts farm yields, food availability, and income, especially among small-scale agricultural producers, where maize is a critical component of food production (Guido et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Challenges in adaptation are compounded by low access to farm inputs and the diverse topographic gradients of the region (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Addressing these challenges required localized and agriculturally appropriate units of analyses to better understand and respond to the impacts of large-scale climate variability and change to consider their impacts on at farm levels.\u003c/p\u003e \u003cp\u003eSmallholder farmers in the region rely heavily on the timing of rainfall, including ROD, RCD, and GSL for planning crop production (Atiah et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mugalavai et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These parameters are crucial for determining planting dates, input distribution, and land preparation, and have a significant impact on crop yields (Gbangou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mugalavai et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sarr, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Variability in ROD and GSL, in particular, affects what and when to plant, making accurate prediction of these events essential for successful crop production (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amekudzi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guido et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral climatic factors, such as seasonal rainfall amount, intra-seasonal rainfall distribution, ROD, and RCD, influence crop growth and development, thereby determining the crop production calendar. ROD is especially critical as it dictates the planting time (Marteau et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and delays in ROD are a reliable indicator of terminal drought in many food-insecure regions of Sub-Saharan Africa (Shukla et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Uncertainty in ROD and RCD can lead to false planting dates, poor growth, crop failure, and lower yields (Akinseye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Early or late planting impacts growth, yield, and farm productivity (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Basu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Eggen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Selvaraju, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, rainfall timing and seasonality are key factors in determining crop types and productivity (Suryabhagavan, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Viste et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccurately predicting the start of the growing season under changing climate conditions can help decision-makers and farmers align planting dates with predicted ROD, enhancing the feasibility and sustainability of rainfed agriculture (Amarasingha et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A lack of objective determination can lead to a significant mismatch between the actual and expect RODs, resulting in false start dates for the upcoming farming season (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guido et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which in turn brings unmet expectations for the ROD.\u003c/p\u003e \u003cp\u003ePrevious studies in western Kenya shown that rainfall beginning dates range from early March to late April (Mugalavai et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the Ethiopian highlands, rainfall timing and seasonality in exhibit significant interannual variability (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which greatly impacts crop yield (Eggen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Existing ROD, RCD, and GSL studies often focus on fragmented geographical locations, political boundaries, or past events, which may not accurately predict future climate conditions. However, the timing and characteristics of the rainy season are shifting, with projections indicating higher precipitation intensity and longer dry spells (Allan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Funk et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Wainwright et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Unpredictable rainfall timing and seasonality could severely impact crop harvests and food supply (Rockstr\u0026ouml;m et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), emphasizing the need for objective information to improve decision making. Thus, this study aims to analyze the observed (1981\u0026ndash;2019) ROD, RCD, and GSL and their future (2020\u0026ndash;2049) changes in maize-based systems in Ethiopia. The results provide tailored information to support decision making, benefiting crop growers, breeders, and policymakers in the region.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Unit of analysis\u003c/h2\u003e \u003cp\u003eThe agroecological zone (AEZ) approach was employed as the unit of analysis, grouping geographical areas with similar climatic conditions that influence their suitability for rainfed agriculture. The study adapted the 2005 FAO agroecological zone classification from the GAEZ v4 Data Portal (fao.org). While maize is cultivated across all 18 conventional AEZs, most of the maize production in a subset of these zone. Developing climate profiles and intervention packages for all maize AEZs presented complexities. Therefore, maize-producing AEZs were regrouped into six clusters based on similarities in rainfall and temperature.\u003c/p\u003e \u003cp\u003eClustering fragmented and patchy maize-producing areas is advantageous for generating decision-making information that is broadly applicable to most maize-producing livelihood systems. Accordingly, moist AEZs in mid- and high-altitude maize-growing areas are designated as 'moist highlands' (MH). Moisture-sufficient AEZs located at low altitudes are referred to as 'moist to sub-humid lowlands' (MSHL). Moisture-deficient AEZs are grouped into 'semi-arid lowlands' (SAL) and 'semi-arid mid-highlands' (SAMH), corresponding to semi-arid lowlands and semi-arid highlands respectively. Low altitude AEZs that are warm and sub-moist are combined and labeled as 'warm sub-moist lowlands' (WSML). AEZs in mid-altitude areas with high moisture and high humidity are clustered and referred to as \u0026lsquo;\u003cem\u003etepid-humid mid-highlands\u003c/em\u003e\u0026rsquo; (THMH). In Rwanda, only two AEZs that dominantly involved in maize production were targeted for this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data type and sources\u003c/h2\u003e \u003cp\u003eDaily rainfall data at a resolution of 0.05\u0026deg;\u0026times;0.05\u0026deg; from the period 1981\u0026ndash;2020 were obtained from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS; Funk et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly daily temperature data at the same resolution for the period 1981\u0026ndash;2016 were taken from the Climate Hazards Centre Climate Infrared Temperature with Stations (CHIRTS) dataset (Funk et al., 2019). Future rainfall and temperature data were derived from the ensemble mean of eight global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6) products, selected for their ability to accurately represent rainfall and temperature patterns, seasonality, and climate teleconnections with ENSO (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data were generated for two emission scenarios: medium forcing (SSP2-4.5) and strong forcing (SSP5-8.5).\u003c/p\u003e \u003cp\u003ePreliminary analysis indicated that both scenarios produced similar results (Appendix Table\u0026nbsp;1), and results obtained only from the strong forcing scenario was considered for this report. Literature also supports the similarity of results from the two scenarios until 2050 (Arnell, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Feleke et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Levy et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Coupled Model Inter-comparison Project Phase 6 (CMIP6) models used in this report\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.875\u0026deg;\u0026times;1.25\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommonwealth Scientific and Industrial Research Organization (Australia)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.125\u0026deg;\u0026times;1.125\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeijing Climate Center (China)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM-CM6-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026deg; \u0026times; 1.4\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u0026eacute;t\u0026eacute;o-France and the European Center for Medium-range Weather Forecast\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026deg;\u0026times;0.70\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean Center Earth Consortium (Europe)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026deg;\u0026times;1\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration (USA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u0026deg;\u0026times;1.41\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapan Agency for Marine-Earth Science and Technology (Japan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.875\u0026deg;\u0026times;1.875\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax Planck Institute for Meteorology (Germany)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKESM1-0-LL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.875\u0026deg; x 1.25\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMet Office Hadley Centre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA quantile delta mapping (QDM) statistical bias correction approach was employed in the analysis to reduce the influence of model systematic errors (Cannon et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Before applying the bias correction, the coarse-scale GCM data were interpolated onto the observation grid, and, if necessary, the time series were linearly adjusted to fit the regular Gregorian calendar (Hempel et al., 2013). For precipitation, a frequency adaptation is used to match the model output's dry-day frequency with the observations (Theme\u0026szlig;l et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Defining the rainfall onset date, cessation date and growing season length\u003c/h2\u003e \u003cp\u003eThere are many definitions for identifying the ROD and RCD (Omay et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In most cases, ROD is either considered the start of the rainy period, based on rainfall data, or it is identified using the evapotranspiration (ET) fraction approach. This approach defines the ROD as the optimal date that ensures sufficient soil moisture during planting and early growing periods to avoid crop failure after sowing (Marteau et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mugalavai et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It requires information on both rainfall and evapotranspiration, as well as to soil characteristics of the study area. A normal cropping period is defined as one where precipitation exceeds potential ET (PET). Such a period meets the ET demands of crops and recharges soil moisture (FAO 1978; 1977; 1986). Accordingly, ROD is defined as the first day when the actual-to-PET ratio is greater than 0.5 for seven consecutive days, followed by a 20-day period in which plant available water remains above wilting in the root zone. RCD is defined as the first day when the actual-to-PET ratio falls below 0.5 for seven consecutive days, followed by 12 consecutive non-growing days in which plant-available water remains below wilting in the root zone. The GSL is then calculated as the total number of days from the rainfall ROD to RCD.\u003c/p\u003e \u003cp\u003eThis method requires daily rainfall and evapotranspiration data, which are often unavailable in many parts of East Africa. As a result, we supplemented our analysis with monthly rainfall and temperature data for each agroecosystem. The monthly evapotranspiration data were calculated from the monthly temperature data for the average latitude of each agroecosystem using the Hargreaves method with the SPEI package in R (Raziei and Pereira, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Santiago Beguer\u0026iacute;ae, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Monthly values of rainfall and PET were plotted, and the start and end of the growing season were defined as the periods when rainfall exceeds or falls below half of PET, respectively. Planting does not begin immediately when rainfall exceeds half of PET, as several days are required for land preparation. Similarly, crop growth may continue for a few days after rainfall falls below 50% of PET due to residual soil moisture, although this duration varies depending on soil type. Thus, the growing season begins a few days after rainfall exceeds half of PET and lasts until a few days after rainfall drops below half of PET.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Analysis of rainfall timing and seasonality in maize growing areas\u003c/h2\u003e \u003cp\u003eUnderstanding rainfall patterns at different spatial scales is crucial for optimizing agricultural practices, mitigating risks, and ensuring food security in regions heavily dependent on agriculture. Such insights enable farmers to make informed decisions regarding planting, irrigation, and harvesting, thereby maximizing yields and minimizing losses. In this context, the results of this analysis are presented across the entire maize-growing landscape, followed by detailed presentations and interpretations of the results within specific AEZs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Rainfall onset date (ROD)\u003c/h2\u003e \u003cp\u003eEarly ROD in Ethiopia begins in the southwest around the mid-February, followed by the southeast and central areas of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). as the rain arrive, they bring much-needed relief to the dry, parched lands, moistening the soil and enabling crop growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2035, most maize-growing areas in Ethiopia are projected to experience a significant delay in rainfall onset, except for a few locations in the central and northern parts of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Notably, areas found in the southwest and eastern parts of the country may experience delays of more than 40 days in rainfall onset compared to the baseline period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Rainfall cessation date (RCD)\u003c/h2\u003e \u003cp\u003eThe pattern of RCDs in Ethiopia largely mirrors that of RODs, with few exceptions in the southwest, where the southern regions experience an earlier start and delayed end to the rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The RCD typically progresses from southward and eastward along the central part of the country, then slowly moves southwestward and westward, concluding around the end of October, with the final cessation of short autumn rains in the extreme southwest by the end November. In 2035, RCD is projected to shift in Ethiopia, with some areas in the southwestern, western, southeastern, and northeastern maize-growing regions experiencing a later cessation date (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Growing season length (GSL)\u003c/h2\u003e \u003cp\u003eThe GSL in Ethiopia's maize-dominated areas ranges from 35 to 288 days. The southwestern and eastern highlands enjoy the longest GSL, and the southern, southeastern, and northeastern parts of the country have the shortest season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Variations in GSL across Ethiopia are influenced by altitude, rainfall patterns, and temperature. Longer seasons in the southwestern and eastern highlands contribute to higher maize yields, while shorter seasons in the southern, southeastern, and northeastern regions present challenges to maize production and productivity.\u003c/p\u003e \u003cp\u003eProjections indicate a reduction in GSL across most maize-growing areas in Ethiopia, except for some parts of the western, southwestern, southeastern, and northeastern regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). These reductions are attributed to climate change impacts. This reduction may exacerbate existing issues of malnutrition and poverty. Overall, projections for both countries are characterized by a delayed onset, earlier cessation, and a general shrinking of GSL in most maize-dominated AEZs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of rainfall timing and seasonality at agroecological zone\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Rainfall onset date (ROD)\u003c/h2\u003e \u003cp\u003eData summarized by the main maize-growing AEZs in Ethiopia indicate that the median mean ROD ranges between March 15 and April 29, with the earliest ROD found in THMH (March 15) and SAL (March 25) AEZs and the late ROD found in MSHL AEZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAEZ-level projected changes in major maize-dominated areas of Ethiopia indicated that ROD would occur later compared to the baseline period in all AEZs, though the magnitude varies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Ethiopia, ROD will be delay by 12 to 46 days by 2035 compared to the baseline period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings underscore the importance of the importance of understanding regional characteristics to tailor solutions effectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Rainfall cessation date (RCD)\u003c/h2\u003e \u003cp\u003eRCDs across Ethiopia\u0026rsquo;s major maize-growing AEZs ranges from September 24 to November 7, with the earliest and latest cessation dates found in the SAMH (September 24) and MSHL (November 7) AEZs, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A summary of the change in RCD across Ethiopia\u0026rsquo;s maize-dominated AEZs shows that all AEZs, except THMH, experienced earlier cessation dates with the most significant shifts in the WSML (earlier by 95 days) and SAMH (earlier 68 days) zones compared to the bassline period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Growing season length (GSL)\u003c/h2\u003e \u003cp\u003eIn Ethiopia, the GSL at the AEZ level ranges from 122 days in SAMH AEZs to 208 days in THMH AEZs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Projections indicate that the GSL will generally decrease compared to the baseline period, though the magnitude of changes varies among AEZs. SAL and WSML AEZs are projected to experience the greatest shrinkage in GSL by 2035, with reductions of 71 and 62 days, respectively, compared to the baseline period. During the baseline period, ROD showed low to moderate interannual variability, with THMH, SAL, and SAMH AEZs in Ethiopia and CSH AEZs in Rwanda exhibiting moderate variability in ROD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Except for the SAMH AEZ, which experiences moderate variability, other AEZs showed low variability in RCD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In 2035, ROD variability is expected to decrease overall, except in the WSML and SAMH AEZs, where only one AEZs (SAMH) will exhibit moderate variability. THMH and SAL AEZs are projected to shift from moderate to low variability in ROD compared to the baseline period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). RCD variability is projected to increase in 2035 across all AEZs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFour AEZs are expected to experience moderate RCD variability, with a general increase in variability compared to the baseline period. GSL variability is expected to increase across all AEZs, with 88% of the AEZs showing moderate variability and three AEZs exhibiting high variability in GSL (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation between rainfall timing and seasonality in maize-based systems\u003c/h2\u003e \u003cp\u003eThe correlation analysis reveals significant variations in rainfall timing and seasonality among maize-based AEZs in Ethiopia (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;c). The correlation between ROD and RCD ranges from \u0026minus;\u0026thinsp;0.1 (THMH) to 0.47 (SAMH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The positive correlation in most AEZs suggests that delayed rainfall onset associated with the delayed rainfall cessation. The correlation between ROD and GSL ranges from \u0026minus;\u0026thinsp;0.48 to 0.05, indicates that delayed rainfall onset contributed to short GSL in MSHL (-0.48), THMH (-0.34), and SAL (-0.25) AEZs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The strong correlation between RCD and GSL indicates that delayed RCD contributes to longer GSL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Rainfall timing and seasonality\u003c/h2\u003e \u003cp\u003eThe early ROD in Ethiopia signals the beginning of the rainy season, allowing farmers to prepare land and sow crops. Delayed ROD is particularly common in the northern regions leads to reduced crop yields and increased water scarcity. Ethiopia's rainy season follows a gradient, with the earliest ROD in the south/southeast and latest in the west/northwest, spanning from February 19 to August 1. A study conducted primarily in the MH AEZ confirmed that ROD ranges between May 18 and June 8, which over a month later than the present findings (April 05) (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This south-to-north progression is influenced by topography and the Inter-Tropical Convergence Zone, which brings moisture-laden winds from the Indian Ocean northwards (Abebe, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This pattern, which aligns with previous studies (Lupi Edao et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Omay et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Segele and Lamb, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wakjira et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), determines planting and harvesting seasons, significantly impacting maize production and socio-economic development in Ethiopia.\u003c/p\u003e \u003cp\u003eProjections indicate that the delayed rainfall onset in Ethiopia's southwest and eastern regions will alter agricultural calendars, impacting farmers' livelihoods and food security. This shift could lead increased vulnerability to drought and reduced yields, posing significant challenges for farmers who rely on maize as a primary source of food and income. Adaptation strategies will be essential to mitigate these impacts.\u003c/p\u003e \u003cp\u003eRCD varies from May 7 to July 30 in southeast and northeast parts, affecting crop planting and harvesting windows. The extreme late RCD, occurring in November in the western part of Ethiopia, can delay crop harvesting and increase the risk of crop damage, affecting overall agricultural productivity. The extreme late rainfall cessation can be attributed to the presence of autumn rainfall in those areas (Wakjira et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another study in MH AEZ indicating that the median RCD ranges between October 9 and October 29 (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding these variations is crucial for developing effective adaptation strategies.\u003c/p\u003e \u003cp\u003eThe GSL in Ethiopia ranges from 35 to 288 days, which is partly consistent with previous studies who reported that the mean GSL ranges 38 to 170 days (Shigute et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, the trend toward shorter GSL in Ethiopia could have significant implications for maize yield and food security, as reduced cultivation time may decrease productivity. Previous studies have confirmed that the GSL increases from the eastern to the western part of the country (Omay et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wakjira et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study in MH AEZ reported a median GSL of 120 to 150 days (Ademe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while another study in the central rift valley (SAL AEZ) reported a GSL ranging 20 and 256 days (Ademe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, a study a study in the MH and SAMH AEZs reported a median GSL of 75 days (Abegaz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Adaptation strategies, such as developing drought-tolerant and early maturing maize varieties, improving irrigation techniques, and adjusting planting times, will be crucial in mitigating these changes. Geographical variations in GSL highlight the importance of understanding local climate and environmental factors while planning agricultural activities.\u003c/p\u003e \u003cp\u003eROD, RCD, and GSL show significant variations among major maize-growing AEZs, affecting agricultural practices. Early rainfall onset in the THMH and SAL AEZs allows for earlier maize planting, leading to longer growing periods and higher yields. In contrast, late onset in the MSHL AEZ may require farmers to adjust planting schedules to avoid water stress and suboptimal crop growth.\u003c/p\u003e \u003cp\u003eThe projected variability in rainfall timing across different AEZs of Ethiopia compared to the baseline period suggests the need for adjustments in planting times and farming methods. In areas with shorter delays, farmers may benefit from earlier planting, while those facing longer delays might need to consider alternative farming methods or irrigation systems. Traditional reliance on predictable rainfall patterns in Ethiopia\u0026rsquo;s maize-dominated agricultural zones is being challenged by climate change, making it difficult for farmers to determine the optimal planting time, leading to reduced yield and potential food insecurity.\u003c/p\u003e \u003cp\u003eThe impact of early or late ROD on maize yields and agricultural practices in different AEZs is significant. For instance, in a highland AEZ with early ROD, farmers can benefit from longer GS and higher yields as they have ample time for crop establishment and growth. This allows them to adopt advanced agricultural practices such as intercropping or precision farming, leading to increased productivity and income. Conversely, in a lowland AEZ with late ROD, farmers face shorter rowing seasons and higher drought risks resulting in lower maize yields and limited agricultural options. Understanding these variations helps farmers make informed decisions about planting times and variety choices (Cui and Xie, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Estimating the median ROD at AEZ level aids in farm planning, identifying drought prone areas, and informing decision on maize variety choice. Improved water management practices and the use of drought-resistant varieties can mitigate these effects.\u003c/p\u003e \u003cp\u003eVariations in RCDs also significantly impact maize farmers in Ethiopia. Farmers in SAMH AEZ must carefully select planting and harvesting times to avoid crop losses, while those reside in MSHL zone, with longer growing periods, face risks from late-season rainfall. Understanding RCD variability is crucial for developing tailored agricultural strategies to mitigate the impacts of climate change on maize production.\u003c/p\u003e \u003cp\u003eGSL shows high spatial variability across AEZ in Ethiopia, significantly influencing agricultural practices and crop yields. Farmers in SAMH AEZ face a shorter growing season, requiring shorter-maturing varieties, while those in the THMH AEZ can grow long-maturing varieties and achieve higher yields.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Implications for maize dominated farming systems\u003c/h2\u003e \u003cp\u003eClimate change is expected to delay ROD, advance RCD, and shorten growing seasons, leading to seasonal water deficit that could severely hamper maize production, especially in AEZs with already short growing seasons in the baseline period (Ademe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, regions traditionally experiencing heavy monsoon rains starting in June may see rainfall delayed until July or later, forcing farmers to delay planting, reducing crop yields. Previous studies have shown that that early ROD is negatively correlated with cereal yield, suggesting the potential for higher yields during early rainfall onset (Kumi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This effect is particularly pronounced in areas receiving unimodal rainfall (Wakjira et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The shift in rainfall patterns necessities adjustments to cropping calendars (Wang et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and effective communication of these changes to facilitate appropriate adaptation strategies. Additionally, climate change may lead to an earlier rainfall cessation, exacerbating water scarcity and causing terminal moisture stresses, further threatening agricultural productivity and food security.\u003c/p\u003e \u003cp\u003eDelayed onset and early cessation of rainfall lead to shorter planting windows, reduced maize coverage, and increased risk of crop failure (Iizumi and Ramankutty, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These changes underscore the urgent need for adaptation strategies and sustainable farming practices to ensure future food production in a changing climate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Possible adaptation options\u003c/h2\u003e \u003cp\u003ePotential interventions are identified from literatures and presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Adaptation options include adjusting planting times, utilizing weather forecasting services, implementing water conservation strategies, and switching to short-maturing varieties in regions where rainfall begins late and ends early, narrowing the growing season. Literature suggests that optimizing planting times is a key adaptation strategy in AEZs experiencing significant delays in rainfall onset (Carr et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Aligning planting schedules with changing rainfall patterns can maximize water use efficiency and improve crop yields. Changing planting window can also mitigate the risk of crop failure due to prolonged dry spells or erratic rainfall patterns.\u003c/p\u003e \u003cp\u003eIntegrated soil fertility management enhances farming system resilience and climate change adaptation (Arumugam et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mubiru et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Techniques such as organic farming and crop rotation improve soil moisture and nutrient retention, making it more resilient to droughts and extreme weather events. In regions with limited rainfall and shortened growing seasons, moisture conservation practices such as efficient irrigation (Kassie et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or rainwater harvesting (Mubiru et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) can reduce water loss and ensure a consistent water supply for crops. Although these methods may involve high upfront costs and maintenance, they offer long-term benefits such as cost savings and increased crop productivity. Conservation practices like mulching (Mubiru et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and cover cropping (Kaye and Quemada, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) help retain soil moisture and reduce evaporation, and improving water infiltration, enhancing water efficiency and sustaining crop production in the face of water scarcity.\u003c/p\u003e \u003cp\u003eSeasonal weather forecasting services and early warning systems are essential for climate change adaptation, enabling farmers to prepare for the upcoming rainy season (Balehegn et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Coughlan de Perez et al., 2022; Lala et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accurate information supports informed decision-making, optimizing planting times and variety choices, and reducing vulnerability to climate-related risks (Agyekum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisks to rainfall timing, hotspot AEZs, suggested adaptation options and their roles in major maize growing AEZs of Ethiopia and Rwanda\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate risks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotspot AEZs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuggested adaptation option\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe role of the option\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLate rainfall onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAll AEZs, especially THMH, SAL, MH and WSML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimizing planting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Synchronize plating date with ROD\u003c/p\u003e \u003cp\u003e\u0026bull; Minimize crop failure and yield reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarr et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Huang et al., (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Kassie et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Mugiyo et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeasonal weather forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Help to make informed decision (when and what to plant)\u003c/p\u003e \u003cp\u003e\u0026bull; Reduce crop failure and farming systems vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgyekum et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Balehegn et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Coughlan de Perez et al. (2022), Lala et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Guido et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClimate risk insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Reduced vulnerability by providing financial compensation for production losses\u003c/p\u003e \u003cp\u003e\u0026bull; Buffer the financial implications of unexpected crop failure due to late rainfall onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Falco et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; OECD, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (World Bank, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEarly rainfall cessation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll except SAL and THMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChoice of early maturing variety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Complete growth in short period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtiah et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Krell et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)(Uwiragiye, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRainwater harvesting and supplementary irrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Support water supply during tasseling and grain filling stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssefa et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Ayele (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Gad\u0026eacute;djisso-Tossou et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShrinkage of growing season length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAll with severe shrinkage in WSML and SAL AEZs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSwitch to medium to early maturing variety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Help to complete its growth with in short time\u003c/p\u003e \u003cp\u003e\u0026bull; Minimize total crop failure or severely reduced yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Id et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Uwiragiye, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClimate smart agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Improve the resilience and adaptive capacity of the system\u003c/p\u003e \u003cp\u003e\u0026bull; Improve yield and farm income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Olayide et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConservation agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Improve the resilience of the system to rainfall variability\u003c/p\u003e \u003cp\u003e\u0026bull; Improve soil moisture conditions\u003c/p\u003e \u003cp\u003e\u0026bull; Reduce soil loss from 35.5 to 14.5 t/ha/year, 50\u0026ndash;70% greater infiltration\u003c/p\u003e \u003cp\u003e\u0026bull; Increase 42% of organic carbon, and\u003c/p\u003e \u003cp\u003e\u0026bull; Increase yield from 3.6 to 4.4t/ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMilder et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Sime et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Kabirigi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eRainfed subsistence agriculture is crucial in East Africa, where crop success heavily depends on rainfall patterns. This study analyzed ROD, RCD, and GSL across major maize-growing AEZs in Ethiopia, using high-resolution historical data (1981–2019) and future projections (2020–2049) from CMIP6 under a strong forcing scenario. Results show significant spatial and temporal variation in ROD, RCD, and GSL. Projections indicate a delayed onset, earlier cessation, and shorter growing seasons, with high interannual variability; signs of increased climate-related risks.\u003c/p\u003e\n\u003cp\u003eCurrently, the rainy season typically begins in May/June and ends in September. Projected shifts may expose maize crops to water stress during critical growth stages, threatening yields. These changes underscore the urgency of revising planting calendars, selecting climate-resilient varieties, and exploring additional adaptation strategies.\u003c/p\u003e\n\u003cp\u003eTo sustain maize production, adaptation must be tailored to each AEZ. Key strategies include adjusting planting dates, adopting suitable maize varieties, improving irrigation, providing seasonal forecasts, climate risk insurance, and promoting climate-smart practices. Seasonal forecasting will become increasingly important as rainfall timing grows more unpredictable.\u003c/p\u003e\n\u003cp\u003eStrong collaboration between policymakers and local communities is essential for developing and scaling site-specific adaptation measures. Prioritizing support for farmers in regions facing the greatest delays in rainfall onset is critical to enhancing resilience and ensuring regional food security.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDA conceived and designed the study, compiled the datasets, performed the analysis, and drafted the manuscript. KT contributed to study design, supported data interpretation, secure funds, supervision, and reviewed the manuscript. BS contributed to the conceptual framework, interpretation of results, supervision, and manuscript revision. AT supported the analysis approach and contributed to manuscript editing. TG contributed to data acquisition/processing and interpretation of results. FG supported data compilation and quality control and contributed to manuscript revision. AN contributed to interpretation of findings and critical revision of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material/Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available. Any processed data files generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for data processing and analysis is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Alliance for a Green Revolution in Africa (AGRA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript and agree to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval/declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like to acknowledge Alliance for Green Revolution Africa (AGRA) for the financial support. We would also like to thank CIMMYT for providing all the necessary facilities require for the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbate, T., Shiferaw, B., Menkir, A., Wegary, D., Kebede, Y., Tesfaye, K., Kassie, M., Bogale, G., Tadesse, B., Keno, T., 2015. Factors that transformed maize productivity in Ethiopia. Food Secur. 7, 965\u0026ndash;981. https://doi.org/10.1007/s12571-015-0488-z\u003c/li\u003e\n\u003cli\u003eAbebe, M., 2006. The Onset , Cessation and Dry Spells of the Small Rainy Season ( Belg ) of Ethiopia.\u003c/li\u003e\n\u003cli\u003eAbegaz, W.B., 2020. Rainfall Variability and Trends over Central Ethiopia. Int. J. Environ. Sci. Nat. Resour. 24. https://doi.org/10.19080/ijesnr.2020.24.556144\u003c/li\u003e\n\u003cli\u003eAdeme, D., Zaitchik, B.F., Tesfaye, K., Simane, B., Alemayehu, G., Adgo, E., 2021. Analysis of agriculturally relevant rainfall characteristics in a tropical highland region: An agroecosystem perspective. Agric. For. Meteorol. 311, 1\u0026ndash;12. https://doi.org/10.1016/j.agrformet.2021.108697\u003c/li\u003e\n\u003cli\u003eAdeme, D., Ziatchik, B.F., Tesfaye, K., Simane, B., Alemayehu, G., Adgo, E., 2020. Climate trends and variability at adaptation scale: Patterns and perceptions in an agricultural region of the Ethiopian Highlands. Weather Clim. Extrem. 29, 100263. https://doi.org/10.1016/j.wace.2020.100263\u003c/li\u003e\n\u003cli\u003eAdeme, F., Kibret, K., Beyene, S., Mitike, G., Getinet, M., 2020. Rainfall analysis for rain-fed farming in the great rift valley basins of Ethiopia. J. Water Clim. Chang. 11, 812\u0026ndash;828. https://doi.org/10.2166/wcc.2019.242\u003c/li\u003e\n\u003cli\u003eAgyekum, T.P., Antwi-Agyei, P., Dougill, A.J., 2022. The contribution of weather forecast information to agriculture, water, and energy sectors in East and West Africa: A systematic review. Front. Environ. Sci. 10, 1\u0026ndash;14. https://doi.org/10.3389/fenvs.2022.935696\u003c/li\u003e\n\u003cli\u003eAkinseye, F.M., Agele, S.O., Traore, P.C.S., Adam, M., Whitbread, A.M., 2016. Evaluation of the onset and length of growing season to define planting date\u0026mdash;\u0026lsquo;a case study for Mali (West Africa).\u0026rsquo; Theor. Appl. Climatol. 124, 973\u0026ndash;983. https://doi.org/10.1007/s00704-015-1460-8\u003c/li\u003e\n\u003cli\u003eAllan, R.P., Barlow, M., Byrne, M.P., Cherchi, A., Douville, H., Fowler, H.J., Gan, T.Y., Pendergrass, A.G., Rosenfeld, D., Swann, A.L.S., Wilcox, L.J., Zolina, O., 2020. Advances in understanding large-scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci. 1472, 49\u0026ndash;75. https://doi.org/10.1111/nyas.14337\u003c/li\u003e\n\u003cli\u003eAmarasingha, R.P.R.K., Galagedara, L.W., Marambe, B., Silva, G.L.L.P., Punyawardena, R., Nidumolu, U., Howden, M., Suriyagoda, L.D.B., 2015. Aligning Sowing Dates with the Onset of Rains to Improve Rice Yields and Water Productivity: Modelling Rice (Oryza sativa L.) Yield of the Maha Season in the Dry Zone of Sri Lanka. Trop. Agric. Res. 25, 277. https://doi.org/10.4038/tar.v25i3.8038\u003c/li\u003e\n\u003cli\u003eAmekudzi, L.K., Yamba, E.I., Preko, K., Asare, E.O., Aryee, J., Baidu, M., Codjoe, S., 2015. Variabilities in Rainfall Onset, Cessation and Length of Rainy Season for the Various Agro-Ecological Zones of Ghana. Climate 3, 416\u0026ndash;434. https://doi.org/10.3390/cli3020416\u003c/li\u003e\n\u003cli\u003eAraya, A., Hoogenboom, G., Luedeling, E., Hadgu, K.M., Kisekka, I., Martorano, L.G., 2015. Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia. Agric. For. Meteorol. 214\u0026ndash;215, 252\u0026ndash;265. https://doi.org/10.1016/j.agrformet.2015.08.259\u003c/li\u003e\n\u003cli\u003eArnell, N.W., 2004. Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob. Environ. Chang. 14, 31\u0026ndash;52. https://doi.org/10.1016/j.gloenvcha.2003.10.006\u003c/li\u003e\n\u003cli\u003eArumugam, P., Chemura, A., Aschenbrenner, P., Schauberger, B., Gornott, C., 2023. Climate change impacts and adaptation strategies: An assessment on sorghum for Burkina Faso. Eur. J. Agron. 142. https://doi.org/10.1016/j.eja.2022.126655\u003c/li\u003e\n\u003cli\u003eAssefa, S., Biazin, B., Muluneh, A., Yimer, F., Haileslassie, A., 2016. Rainwater harvesting for supplemental irrigation of onions in the southern dry lands of Ethiopia. Agric. Water Manag. 178, 325\u0026ndash;334. https://doi.org/10.1016/j.agwat.2016.10.012\u003c/li\u003e\n\u003cli\u003eAtiah, W.A., Muthoni, F.K., Kotu, B., Kizito, F., Amekudzi, L.K., 2021. Trends of rainfall onset, cessation, and length of growing season in northern ghana: Comparing the rain gauge, satellite, and farmer\u0026rsquo;s perceptions. Atmosphere (Basel). 12. https://doi.org/10.3390/atmos12121674\u003c/li\u003e\n\u003cli\u003eAyele, Y.A., 2014. Rainwater Harvesting for Climate Change Adaptation in Ethiopia: Policy and Institutional Analysis.\u003c/li\u003e\n\u003cli\u003eBalehegn, M., Balehey, S., Fu, C., Liang, W., 2019. Indigenous weather and climate forecasting knowledge among Afar pastoralists of north eastern Ethiopia: Role in adaptation to weather and climate variability. Pastoralism 9. https://doi.org/10.1186/s13570-019-0143-y\u003c/li\u003e\n\u003cli\u003eBaptista, D., Farid, M., 2022. Climate Change and Chronic Food Insecurity in Sub-Saharan Africa.\u003c/li\u003e\n\u003cli\u003eBasu, S., Ramegowda, V., Kumar, A., Pereira, A., 2016. Plant adaptation to drought stress. F Res. 5, 1\u0026ndash;10. https://doi.org/10.12688/F1000RESEARCH.7678.1\u003c/li\u003e\n\u003cli\u003eBirhan, D.A., Zaitchik, B.F., Fantaye, K.T., Birhanu, B.S., Damot, G.A., Tsegaye, E.A., 2022. Observed and projected trends in climate extremes in a tropical highland region: An agroecosystem perspective. Int. J. Climatol. 42, 2493\u0026ndash;2513. https://doi.org/10.1002/joc.7378\u003c/li\u003e\n\u003cli\u003eBucagu, C., Ndoli, A., Cyamweshi, A.R., Nabahungu, L.N., Mukuralinda, A., Smethurst, P., 2020. Determining and managing maize yield gaps in Rwanda. Food Secur. 12, 1269\u0026ndash;1282. https://doi.org/10.1007/s12571-020-01059-2\u003c/li\u003e\n\u003cli\u003eCairns, J.E., Hellin, J., Sonder, K., Araus, J.L., MacRobert, J.F., Thierfelder, C., Prasanna, B.M., 2013. Adapting maize production to climate change in sub-Saharan Africa. Food Secur. 5, 345\u0026ndash;360. https://doi.org/10.1007/s12571-013-0256-x\u003c/li\u003e\n\u003cli\u003eCannon, A.J., Sobie, S.R., Murdock, T.Q., 2015. Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938\u0026ndash;6959. https://doi.org/10.1175/JCLI-D-14-00754.1\u003c/li\u003e\n\u003cli\u003eCarr, T.W., Mkuhlani, S., Segnon, A.C., Ali, Z., Zougmor\u0026eacute;, R., Dangour, A.D., Green, R., Scheelbeek, P., 2022. Climate change impacts and adaptation strategies for crops in West Africa: A systematic review. Environ. Res. Lett. 17. https://doi.org/10.1088/1748-9326/ac61c8\u003c/li\u003e\n\u003cli\u003eChawarska, K., Klin, A., Paul, R., Macari, S., Volkmar, F., 2009. A prospective study of toddlers with ASD: Short-term diagnostic and cognitive outcomes. J. Child Psychol. Psychiatry Allied Discip. 50, 1235\u0026ndash;1245. https://doi.org/10.1111/j.1469-7610.2009.02101.x\u003c/li\u003e\n\u003cli\u003eChrysostom, N.J., 2014. Analysis of economic efficiency of maize production in Huye district in Rwanda. Int. J. Agric. Innov. Res. 3, 707\u0026ndash;716.\u003c/li\u003e\n\u003cli\u003eClaude, R., 2017. Maize in Rwanda : A Value Chain Analysis. Univ. Nairobi 1, 9.\u003c/li\u003e\n\u003cli\u003eContext Network, 2016. Rwanda Early Generation Seed Study: Country Report 1\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eCoughlan de Perez, E., Harrison, L., Berse, K., Easton-Calabria, E., Marunye, J., Marake, M., Murshed, S.B., Shampa, Zauisomue, E.H., 2022. Adapting to climate change through anticipatory action: The potential use of weather-based early warnings. Weather Clim. Extrem. 38, 100508. https://doi.org/10.1016/j.wace.2022.100508\u003c/li\u003e\n\u003cli\u003eCSA, 2022. AGRICULTURAL SAMPLE SURVEY AREA AND PRODUCTION OF MAJOR CROPS.\u003c/li\u003e\n\u003cli\u003eCSA, 2018. The Federal Democratic Republic of Ethiopia Centeral Statistical Agency Agricultural Sample Survey 2017/18 (2010 E.C.): Report on area and production for major crops (private peasant holdings, Meher season), Statistical Bulletin No. 586.\u003c/li\u003e\n\u003cli\u003eCSA, 2016. Agricultural sample surveys:2015/2016, Statistical Bulletin No. 584. https://doi.org/10.1038/155183a0\u003c/li\u003e\n\u003cli\u003eCSA, 2015. Agricultural Sample Survey Time Series Data for National \u0026amp; Regional Level (1995/96-2014/15).\u003c/li\u003e\n\u003cli\u003eCui, X., Xie, W., 2022. Adapting Agriculture to Climate Change through Growing Season Adjustments: Evidence from Corn in China. Am. J. Agric. Econ. 104, 249\u0026ndash;272. https://doi.org/10.1111/ajae.12227\u003c/li\u003e\n\u003cli\u003eDegebasa, L., Tessema, T., Bekeko, Z., Belete, K., 2022. Prevalence and Socioeconomic Impact of Striga (Striga hermonthica) in Sorghum Producing Areas of East and West Hararghe Zones, Oromia, Ethiopia. Int. J. Agron. 2022. https://doi.org/10.1155/2022/8402280\u003c/li\u003e\n\u003cli\u003eDemissie, G., Deressa, T., Haile, M., Bogale, G., Dida, M., Chibsa, T., Gedefaw, Y., Tulu, D., Debelo, M., Keno, T., Azmach, and G., 2016. Prevalence, Distribution and Impact of Maize Lethal Necrosis Disease (MLND) in Ethiopia. Pest Manag. J. Ethiop. 18, 37\u0026ndash;49.\u003c/li\u003e\n\u003cli\u003eDunning, C.M., Black, E.C.L., Allan, R.P., 2016. The onset and cessation of seasonal rainfall over Africa. J. Geophys. Res. 121, 11405\u0026ndash;11424. https://doi.org/10.1002/2016JD025428\u003c/li\u003e\n\u003cli\u003eEggen, M., Ozdogan, M., Zaitchik, B., Ademe, D., Foltz, J., Simane, B., 2019. Vulnerability of sorghum production to extreme, sub-seasonal weather under climate change. Environ. Res. Lett. 14, 045005. https://doi.org/10.1088/1748-9326/aafe19\u003c/li\u003e\n\u003cli\u003eFalco, S. Di, Adinolfi, F., Bozzola, M., Capitanio, F., 2014. Crop Insurance as a Strategy for Adapting to Climate Change. J. Agric. Econ. 65, 485\u0026ndash;504. https://doi.org/10.1111/1477-9552.12053\u003c/li\u003e\n\u003cli\u003eFAO, 2019. GIEWS Country Brief Zambia, Glogbal Watch GIEWS.\u003c/li\u003e\n\u003cli\u003eFeleke, H.G., Savage, M.J., Fantaye, K.T., Rettie, F.M., 2023. The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia. Atmosphere (Basel). 14, 497. https://doi.org/10.3390/atmos14030497\u003c/li\u003e\n\u003cli\u003eFriesen, D. k., Palmer, A.F.E., 2002. Integrated Approaches to Higher Maize Productivity in the New Millennium, In Friesen DK and Palmer AFE (2002) Proceedings of the Seventh Eastern and Southern Africa Regional Maize Conference Nairobi, Kenya 5 \u0026ndash; 11 February 2002.\u003c/li\u003e\n\u003cli\u003eFunk, C., Harrison, L., Alexander, L., Peterson, P., Behrangi, A., Husak, G., 2019a. Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions. Environ. Res. Lett. 14. https://doi.org/10.1088/1748-9326/ab4a6c\u003c/li\u003e\n\u003cli\u003eFunk, C., Peterson, P., Peterson, S., Shukla, S., Davenport, F., Michaelsen, J., Knapp, K.R., Landsfeld, M., Husak, G., Harrison, L., Rowland, J., Budde, M., Meiburg, A., Dinku, T., Pedreros, D., Mata, N., 2019b. A high-resolution 1983\u0026ndash;2016 TMAX climate data record based on infrared temperatures and stations by the climate hazard center. J. Clim. 32, 5639\u0026ndash;5658. https://doi.org/10.1175/JCLI-D-18-0698.1\u003c/li\u003e\n\u003cli\u003eFunk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., Verdin, A.P., 2014. A Quasi-Global Precipitation Time Series for Drought Monitoring. U.S. Geol. Surv. Data Ser. 832, 4.\u003c/li\u003e\n\u003cli\u003eGad\u0026eacute;djisso-Tossou, A., Avell\u0026aacute;n, T., Sch\u0026uuml;tze, N., 2018. Potential of deficit and supplemental irrigation under climate variability in northern Togo, West Africa. Water (Switzerland) 10. https://doi.org/10.3390/w10121803\u003c/li\u003e\n\u003cli\u003eGbangou, T., Ludwig, F., van Slobbe, E., Hoang, L., Gordana Kranjac-Berisavljevic, Slobbe, E. Van, Hoang, L., 2019. Seasonal variability and predictability of agro-meteorological indices: Tailoring onset of rainy season estimation to meet farmers\u0026rsquo; needs in Ghana. Clim. Serv. 14, 19\u0026ndash;30. https://doi.org/10.1016/j.cliser.2019.04.002\u003c/li\u003e\n\u003cli\u003eGezahegn, B., Gebire, W., Bamud, K., 2018. Identification of major crop production constraints in debub ari district, south omo zone, southern Ethiopia. Hortic. Int. J. 2, 383\u0026ndash;388. https://doi.org/10.15406/hij.2018.02.00081\u003c/li\u003e\n\u003cli\u003eGinbo, T., 2022. Heterogeneous impacts of climate change on crop yields across altitudes in Ethiopia. Clim. Change 170. https://doi.org/10.1007/s10584-022-03306-1\u003c/li\u003e\n\u003cli\u003eGuido, Z., Zimmer, A., Lopus, S., Hannah, C., Gower, D., Waldman, K., Krell, N., Sheffield, J., Caylor, K., Evans, T., 2020. Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa. Clim. Risk Manag. 30, 100247. https://doi.org/10.1016/j.crm.2020.100247\u003c/li\u003e\n\u003cli\u003eHempel, S., Frieler, K., Warszawski, L., Schewe, J., Piontek, F., 2013. A trend-preserving bias correction \u0026amp;ndash; The ISI-MIP approach. Earth Syst. Dyn. 4, 219\u0026ndash;236. https://doi.org/10.5194/esd-4-219-2013\u003c/li\u003e\n\u003cli\u003eHerve, M., 2019. Investigating the Effect of Climate Variability and Change on Maize Yield in Rwanda. University of Nirobi.\u003c/li\u003e\n\u003cli\u003eHuang, M., Wang, J., Wang, B., Liu, D.L., Yu, Q., He, D., Wang, N., Pan, X., 2020. Optimizing sowing window and cultivar choice can boost China\u0026rsquo;s maize yield under 1.5 \u0026deg;C and 2 \u0026deg;C global warming. Environ. Res. Lett. 15. https://doi.org/10.1088/1748-9326/ab66ca\u003c/li\u003e\n\u003cli\u003eHunter, R., Crespo, O., Coldrey, K., Cronin, K., New, M., 2020. Climate Change and Future Crop Suitability in Rwanda. Cape Town.\u003c/li\u003e\n\u003cli\u003eId, D.M., Id, W.W., Mamo, G., 2023. Spatio-temporal variability and rainfall trend affects seasonal calendar of maize production in southern central Rift Valley of Ethiopia. PLOS Clim. 1\u0026ndash;31. https://doi.org/10.1371/journal.pclm.0000218\u003c/li\u003e\n\u003cli\u003eIizumi, T., Ramankutty, N., 2015. How do weather and climate influence cropping area and intensity? Glob. Food Sec. 4, 46\u0026ndash;50. https://doi.org/10.1016/j.gfs.2014.11.003\u003c/li\u003e\n\u003cli\u003eKabirigi, M., Musana, B., Ngetich, F., Mugwe, J., Mukuralinda, A., Nabahungu, N.L., 2015. Applicability of conservation agriculture for climate change adaptation in Rwanda\u0026rsquo;s situation. J. soil Sci. Environ. Manag. 6, 241\u0026ndash;248. https://doi.org/10.5897/JSSEM15.0508\u003c/li\u003e\n\u003cli\u003eKassie, B.T., Asseng, S., Rotter, R.P., Hengsdijk, H., Ruane, A.C., Van Ittersum, M.K., 2015. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145\u0026ndash;158. https://doi.org/10.1007/s10584-014-1322-x\u003c/li\u003e\n\u003cli\u003eKassie, B.T., Hengsdijk, H., R\u0026ouml;tter, R., Kahiluoto, H., Asseng, S., Van Ittersum, M., 2013. Adapting to climate variability and change: Experiences from cereal-based farming in the central rift and kobo valleys, Ethiopia. Environ. Manage. 52, 1115\u0026ndash;1131. https://doi.org/10.1007/s00267-013-0145-2\u003c/li\u003e\n\u003cli\u003eKassie, B.T., Van Ittersum, M.K., Hengsdijk, H., Asseng, S., Wolf, J., R\u0026ouml;tter, R.P., 2014. Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia. F. Crop. Res. 160, 41\u0026ndash;53. https://doi.org/10.1016/j.fcr.2014.02.010\u003c/li\u003e\n\u003cli\u003eKaye, J.P., Quemada, M., 2017. Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev. 37. https://doi.org/10.1007/s13593-016-0410-x\u003c/li\u003e\n\u003cli\u003eKindie Tesfaye, Sika Gbegbelegbe, Jill E. Cairns, Bekele Shiferaw, Boddupalli M. Prasanna, Kai Sonder, Ken Boote, Dan Makumbi, R.R., 2015. Maize systems under climate change in sub-Saharan Africa Potential impacts on production and food security. Int. J. Clim. Chang. Strateg. Manag. 7. https://doi.org/10.1108/17568690910955603\u003c/li\u003e\n\u003cli\u003eKrell, N.T., Morgan, B.E., Gower, D., Caylor, K.K., 2021. Consequences of Dryland Maize Planting Decisions Under Increased Seasonal Rainfall Variability. Water Resour. Res. 57, 1\u0026ndash;28. https://doi.org/10.1029/2020WR029362\u003c/li\u003e\n\u003cli\u003eKumi, N., Adeliyi, T.E., Asante, V.A., Abiodun, B.J., Lamptey, B.L., 2023. Impact of rainfall onset date on crops yield in Ghana. Front. Sustain. Food Syst. 7, 1\u0026ndash;10. https://doi.org/10.3389/fsufs.2023.1176385\u003c/li\u003e\n\u003cli\u003eLala, J., Yang, M., Wang, G., Block, P., 2021. Utilizing rainy season onset predictions to enhance maize yields in Ethiopia. Environ. Res. Lett. 16. https://doi.org/10.1088/1748-9326/abf9c9\u003c/li\u003e\n\u003cli\u003eLevy, P.E., Cannell, M.G.R., Friend, A.D., 2004. Modelling the impact of future changes in climate, CO2 concentration and land use on natural ecosystems and the terrestrial carbon sink. Glob. Environ. Chang. 14, 21\u0026ndash;30. https://doi.org/10.1016/j.gloenvcha.2003.10.005\u003c/li\u003e\n\u003cli\u003eLupi Edao, A., Kibert, K., Mamo, G., 2018. Analysis of Start, End and Length of the Growing Season and Number of Rainy Days in Semi-Arid Central Refit Valley of Oromia State, Ethiopia. Adv. Crop Sci. Technol. 06. https://doi.org/10.4172/2329-8863.1000386\u003c/li\u003e\n\u003cli\u003eMarteau, R., Sultan, B., Moron, V., Alhassane, A., Baron, C., Traor\u0026eacute;, S.B., 2011. The onset of the rainy season and farmers\u0026rsquo; sowing strategy for pearl millet cultivation in Southwest Niger. Agric. For. Meteorol. 151, 1356\u0026ndash;1369. https://doi.org/10.1016/j.agrformet.2011.05.018\u003c/li\u003e\n\u003cli\u003eMellander, P.E., Gebrehiwot, S.G., G\u0026auml;rden\u0026auml;s, A.I., Bewket, W., Bishop, K., 2013. Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns. PLoS One 8, 1\u0026ndash;12. https://doi.org/10.1371/journal.pone.0068461\u003c/li\u003e\n\u003cli\u003eMerga, T., Zhang, W., Shuaixiang, Z., Zhiping, D., 2022. Enhancing maize yield in Ethiopia a. Int. J. Agric. Sci. Food Technol. 8, 193\u0026ndash;201.\u003c/li\u003e\n\u003cli\u003eMilder, J.C., Majanen, T., Scherr, S.J., 2012. Performance and Potential of Conservation Agriculture for Climate Change Adaptation and Mitigation in Sub-Saharan Africa An assessment of WWF and CARE projects in support of the WWF-CARE Alliance\u0026rsquo;s Rural Futures Initiative.\u003c/li\u003e\n\u003cli\u003eMohammed, M., 2021. Empirical Analysis of the Factors that Affecting Maize Production of Farmers among Smallholders: The Case of Eastern Oromia, Ethiopia. Turkish J. Agric. - Food Sci. Technol. 9, 137\u0026ndash;145. https://doi.org/10.24925/turjaf.v9i1.137-145.3769\u003c/li\u003e\n\u003cli\u003eMosisa Worku, Hussein Shimelis, A., Laing, M., 2013. Preferences and constraints of maize farmers in the development and adoption of improved varieties in the mid-altitude, sub-humid agro-ecology of Western Ethiopia. African J. Agric. Res. 8, 1245\u0026ndash;1254. https://doi.org/10.5897/ajar12.1972\u003c/li\u003e\n\u003cli\u003eMubiru, D.N., Kyazze, F.B., Radeny, M., Zziwa, A., Lwasa, J., Kinyangi, J., 2015. Climatic trends, risk perceptions and coping strategies of smallholder farmers in rural Uganda. CCAFS Work. Pap. 41 pp.\u003c/li\u003e\n\u003cli\u003eMugalavai, E.M., Kipkorir, E.C., Raes, D., Rao, M.S., 2008. Analysis of rainfall onset , cessation and length of growing season for western Kenya 148, 1123\u0026ndash;1135. https://doi.org/10.1016/j.agrformet.2008.02.013\u003c/li\u003e\n\u003cli\u003eMugiyo, H., Mhizha, T., Chimonyo, V.G.P., Mabhaudhi, T., 2021. Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe. Heliyon 7, e06109. https://doi.org/10.1016/j.heliyon.2021.e06109\u003c/li\u003e\n\u003cli\u003eMulungu, K., N. Ng\u0026rsquo;ombe, J., 2020. Climate Change Impacts on Sustainable Maize Production in Sub-Saharan Africa: A Review. Maize - Prod. Use 1\u0026ndash;11. https://doi.org/10.5772/intechopen.90033\u003c/li\u003e\n\u003cli\u003eNational Institute of Statstics of Rwanda, 2019. Seasonal Agricultural Survey 2019.\u003c/li\u003e\n\u003cli\u003eNgango, J., Hong, S., 2021. Improving farm productivity through the reduction of managerial and technology gaps among farmers in Rwanda. Agric. Food Secur. 10, 1\u0026ndash;14. https://doi.org/10.1186/s40066-020-00284-4\u003c/li\u003e\n\u003cli\u003eOECD, 2023. Enhancing the insurance sector\u0026rsquo;s contribution to climate adaptation.\u003c/li\u003e\n\u003cli\u003eOlayide, O.E., Tetteh, I.K., Popoola, L., 2016. Differential impacts of rainfall and irrigation on agricultural production in Nigeria: Any lessons for climate-smart agriculture? Agric. Water Manag. 178, 30\u0026ndash;36. https://doi.org/10.1016/j.agwat.2016.08.034\u003c/li\u003e\n\u003cli\u003eOmay, P.O., Muthama, N.J., Oludhe, C., Kinama, J.M., Artan, G., Atheru, Z., 2023. Changes and variability in rainfall onset, cessation, and length of rainy season in the IGAD region of Eastern Africa. Theor. Appl. Climatol. 152, 871\u0026ndash;893. https://doi.org/10.1007/s00704-023-04433-0\u003c/li\u003e\n\u003cli\u003eRaziei, T., Pereira, L.S., 2013. Estimation of ETo with Hargreaves-Samani and FAO-PM temperature methods for a wide range of climates in Iran. Agric. Water Manag. 121, 1\u0026ndash;18. https://doi.org/10.1016/j.agwat.2012.12.019\u003c/li\u003e\n\u003cli\u003eReport, A. project progress, 2021. Increasing maize value chain efficiency in Rwanda.\u003c/li\u003e\n\u003cli\u003eRockstr\u0026ouml;m, J., Karlberg, L., Wani, S.P., Barron, J., Hatibu, N., Oweis, T., Bruggeman, A., Farahani, J., Qiang, Z., 2010. Managing water in rainfed agriculture-The need for a paradigm shift. Agric. Water Manag. 97, 543\u0026ndash;550. https://doi.org/10.1016/j.agwat.2009.09.009\u003c/li\u003e\n\u003cli\u003eRwanda Meteorology Agency, 2022. Season A 2022 . Issue No . 01 January 2022 Rwanda National Crop Monitor Season A 2022 . Issue No . 01 January 2022 Rwanda National Crop Monitor.\u003c/li\u003e\n\u003cli\u003eSantiago Beguer\u0026iacute;ae, S.M.V.-S., 2023. Package \u0026lsquo; SPEI \u0026rsquo;: Calculation of the Standardized Precipitation-Evapotranspiration Index.\u003c/li\u003e\n\u003cli\u003eSarr, B., 2012. Present and future climate change in the semi-arid region of West Africa : a crucial input for practical adaptation in. Atmos. Sci. Lett. 13, 108\u0026ndash;112. https://doi.org/10.1002/asl.368\u003c/li\u003e\n\u003cli\u003eSegele, Z.T., Lamb, P.J., 2005. Characterization and variability of Kiremt rainy season over Ethiopia. Meteorol. Atmos. Phys. 89, 153\u0026ndash;180. https://doi.org/10.1007/s00703-005-0127-x\u003c/li\u003e\n\u003cli\u003eSelvaraju, R., 2011. Climate risk assessment and management in agriculture. Build. Resil. Adapt. to Clim. Chang. Agric. Sect. 23, 71\u0026ndash;90. https://doi.org/doi:10.1103/PhysRevD.64.104020\u003c/li\u003e\n\u003cli\u003eSenapathy, M., 2021. Evaluation of Maize (Zea mays L.) Varieties for Moisture Stress Areas in Humbo District, Wolaita Zone, Southern Ethiopia. Int. J. Agric. Environ. Biotechnol. 14, 75\u0026ndash;82. https://doi.org/10.30954/0974-1712.01.2021.8\u003c/li\u003e\n\u003cli\u003eShah, H., Hellegers, P., Siderius, C., 2021. Climate risk to agriculture: A synthesis to define different types of critical moments. Clim. Risk Manag. 34, 100378. https://doi.org/10.1016/j.crm.2021.100378\u003c/li\u003e\n\u003cli\u003eShigute, M., Alamirew, T., Abebe, A., Ndehedehe, C.E., Kassahun, H.T., 2023. Analysis of rainfall and temperature variability for agricultural water management in the upper Genale river basin, Ethiopia. Sci. African 20. https://doi.org/10.1016/j.sciaf.2023.e01635\u003c/li\u003e\n\u003cli\u003eShukla, S., Husak, G., Turner, W., Davenport, F., Funk, C., Harrison, L., Krell, N., 2021. A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa. PLoS One 16, 1\u0026ndash;21. https://doi.org/10.1371/journal.pone.0242883\u003c/li\u003e\n\u003cli\u003eSime, G., Aune, J.B., Mohammed, H., 2015. Agronomic and economic response of tillage and water conservation management in maize, central rift valley in Ethiopia. Soil Tillage Res. 148, 20\u0026ndash;30. https://doi.org/10.1016/j.still.2014.12.001\u003c/li\u003e\n\u003cli\u003eStuch, B., Alcamo, J., Schaldach, R., 2021. Projected climate change impacts on mean and year-to-year variability of yield of key smallholder crops in Sub-Saharan Africa. https://doi.org/10.1080/17565529.2020.1760771\u003c/li\u003e\n\u003cli\u003eSuryabhagavan, K. V., 2017. GIS-based climate variability and drought characterization in Ethiopia over three decades. Weather Clim. Extrem. 15, 11\u0026ndash;23. https://doi.org/10.1016/j.wace.2016.11.005\u003c/li\u003e\n\u003cli\u003eTesfaye, K., Gbegbelegbe, S., Cairns, J.E., Shiferaw, B., Prasanna, B.M., Sonder, K., Boote, K., Makumbi, D., Robertson, R., 2015. Maize systems under climate change in sub-Saharan Africa: Potential impacts on production and food security. Int. J. Clim. Chang. Strateg. Manag. 7, 247\u0026ndash;271. https://doi.org/10.1108/IJCCSM-01-2014-0005\u003c/li\u003e\n\u003cli\u003eTheme\u0026szlig;l, M.J., Gobiet, A., Heinrich, G., 2012. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim. Change 112, 449\u0026ndash;468. https://doi.org/10.1007/s10584-011-0224-4\u003c/li\u003e\n\u003cli\u003eUrgessa, T.B., 2014. Review of challenges and prospects of agricultural production and productivity in Ethiopia. J. Nat. Sci. Res. 4, 70\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eUwiragiye, A., 2016. Assessing the Impact of Climate Change and Variability on Wetland Maize Production and Food Security in Highlands and Central Plateaus of Rwanda Case Study of Bahimba and Bishenyi Wetlands. UR Libr. 11, 31\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eViste, E., Korecha, D., Sorteberg, A., 2013. Recent drought and precipitation tendencies in Ethiopia. Theor. Appl. Climatol. 112, 535\u0026ndash;551. https://doi.org/10.1007/s00704-012-0746-3\u003c/li\u003e\n\u003cli\u003eWainwright, C.M., Black, E., Allan, R.P., 2021. Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 simulations. J. Hydrometeorol. 2339\u0026ndash;2357. https://doi.org/10.1175/jhm-d-21-0017.1\u003c/li\u003e\n\u003cli\u003eWakjira, M.T., Peleg, N., Anghileri, D., Molnar, D., Alamirew, T., Six, J., Molnar, P., 2021. Rainfall seasonality and timing: implications for cereal crop production in Ethiopia. Agric. For. Meteorol. 310, 108633. https://doi.org/10.1016/j.agrformet.2021.108633\u003c/li\u003e\n\u003cli\u003eWang, X., Folberth, C., Skalsky, R., Wang, S., Chen, B., Liu, Y., Chen, J., Balkovic, J., 2022. Crop calendar optimization for climate change adaptation in rice-based multiple cropping systems of India and Bangladesh. Agric. For. Meteorol. 315, 108830. https://doi.org/10.1016/j.agrformet.2022.108830\u003c/li\u003e\n\u003cli\u003eWFP, 2021. Evaluation Brief.\u003c/li\u003e\n\u003cli\u003eWorld Bank, 2009. Making Development Climate Resilient. A World Bank Strategy for Sub-Saharan Africa, Report No. 46947-AFR.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adaptation, agroecological zones, climate-smart agriculture, East African farming systems, maize yield","lastPublishedDoi":"10.21203/rs.3.rs-8921665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8921665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRainfed subsistence agriculture is vital for food security in East Africa, particularly in regions where maize is the dominant crop. Its success depends heavily on rainfall patterns, making it vulnerable to climate variability and change. This study analyzes the variability in Rainfall Onset Date (ROD), Rainfall Cessation Date (RCD), and Growing Season Length (GSL) across major maize-growing agroecological zones (AEZs) in Ethiopia. It also examines the potential impacts of future climate scenarios and identifies adaptation strategies to reduce risks to maize production. High-resolution historical data (1981\u0026ndash;2019) and projections from CMIP6 models under the SSP5-8.5 scenario (2020\u0026ndash;2049) were used. The results show significant variation in ROD, RCD, and GSL between AEZs and climate periods. Future projections indicate delayed ROD (by 10 to 49 days), earlier RCD (by 6 to 95 days), and a shorter GSL (by 10 to 71 days), with high interannual variability. These shifts may expose maize crops to water stress during critical growth stages, increasing drought vulnerability and reducing yields. To sustain production, agricultural practices must be reevaluated. Recommended adaptation strategies include adjusting planting dates, adopting drought-tolerant and early-maturing maize varieties, and improving irrigation efficiency. Revising cropping calendars and promoting collaboration among policymakers, researchers, and farming communities are essential to developing effective, site-specific responses. These efforts are critical to strengthening the resilience of maize-based farming systems and ensuring food security under changing climatic conditions in Ethiopia and the wider East African region.\u003c/p\u003e","manuscriptTitle":"Climate change affects rainfall seasonality and timing in maize-based agroecological zones of Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 17:30:14","doi":"10.21203/rs.3.rs-8921665/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T10:14:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T06:49:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T06:47:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-02-20T01:57:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"90fa28f1-4781-4a83-9bc2-f85951264b33","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T08:41:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 17:30:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8921665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8921665","identity":"rs-8921665","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}