Analysis of rainfall variability and trends for better climate risk management in the major maize producing districts in northwestern part of Ethiopia

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The rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion. The lowest CV values for the seasonal fluctuation of rainfall during the kiremt season are 7.7 at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. The CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that there is greater temporal variability in the total rainfall during the bega and belg seasons. At Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. The average rainy season began on May 21 (142.3 DOY) in Alefa and ended on June 12 (164.2 DOY) in Chilga. On the other hand, the rainy season ends November 3 (308 DOY), November 4 (309 DOY), and November 12 (317 DOY) in Alefa, Chilga, and Maksegnit, respectively. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At Alefa, the likelihood of dry periods lasting more than five days steadily reduces starting on May 21, October 12, and March 1, and then gradually increases again around October 17 and November 1.Therfore, this findings give a clue of understanding the rainfall features and associated to crop production in the study area. Rainfall Variability onset cessation and dry spell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Global atmospheric and oceanic circulations are among the factors that contribute to fluctuations in weather variables such as temperature, atmospheric pressure, and rainfall (Endris et al. 2018 ). Increased in the hydrological cycle, variability in amount and distribution of rainfall and occurrence of extreme events in many parts of the globe are intense indications of global climate change and climate variability (Merabtene et al., 2016 ). Climate variability and change are major threats for developing countries, especially for the people of sub-Saharan Africas (SSA) (Barana, 2017). As reported by Mirza ( 2003 ) and Gemeda and Sima ( 2015 ), climate change and variability brought back the nations development by reducing crop yield and aggravates food insecurity. In the Sub-Saharan Africa Rainfall anomaly is likely to increase while its amount is expected to decrease (IPCC, 2014). The same report explains that, the rising global temperature attributed to the greenhouse gases emission is unequivocal that in turn affects the rainfall anomalies. Higher temperature throughout SSA are causing increased evapotranspiration, shorter growing seasons, drying of the soil, increased pest and disease pressure, shifting in suitability areas for growing crops and livestock, and other direct and indirect impact for agriculture (Cline, 2017; Lobell et al., 2008 ). Climate change is also cause increased variability of rainfall in much of SSA, and increased intensity and frequency of extreme events, including droughts, floods and storms. Climate change is a global phenomenon, but its effect is region and location specific; this makes the recent climate studies have been mostly focusing on small scale that provides more detail information for a better management and planning of local resources (Ramli et al ., 2019; Elsanabary and Gan, 2015 ). SSA including Ethiopia is very vulnerable to climate change and variability as its economy is largely depend on whether sensitive agriculture (Hope, 2009 ). According to (Mekonnen Yibrah et al., 2019 ), agriculture plays a dominant role in the economy of Ethiopia, contributing for national GDP, 80% of the employment and the majority of foreign exchange earnings. In Ethiopia is this sector is highly susceptible to the effect of climate variability with wide gap studies at local level (Degife et al., 2021 ). The productivity of the rainfed farming system is determined by the temporal distribution of rainfall with respect to the cropping period in a given hydrological year (Guhathakurta and Saji, 2013 ; Hao et al., 2013 ; Mosisa et al ., 2021) because this controls the amount of water stored in the soil that is available for biomass production. As a result of climate variability, a significant shift in the pattern of rainfall distribution is expected to occur in the coming decades (Beweket, 2009). These shifts in the amount and intensity of rainfall are also projected to affect agricultural productivity, land suitability and welfare levels of households which derive their livelihoods from agriculture. Moreover, rainfall variability affects agriculture through reduced precipitation and increased evapotranspiration as an indirect result of a change in climatic variables other than the direct impacts on temperature and rainfall. Proper understanding of climate variability is vital for better climate risk management in various sectors of the economy and more importantly for agriculture, which is the majority of the community are engaged and their livelihood is highly dependent on it (Mittal & Hariharan, 2018 ; van Huysen et al., 2018 ). Assessing climate risk and developing proper tactical and strategic management options in agriculture is impossible without adequate knowledge of climatic conditions acquired through critical analyses of variability and trends in the historical climatic conditions for major agricultural activities at the particular location of interest (Meybeck et al., 2012 ). Rainfall variability at a global scale over time and space affects all aspects of human activity, especially agricultural economies and social activities (Asmaa et al ., 2020). In particular, rainfall is the most significant meteorological parameter in Ethiopia, as approximately 85% of the Ethiopian labor force is employed in rain-fed agriculture which highly depends on low or high amounts of rainfall availability vital for crop production (Diro et al., 2011 ). The trend of rainfall in Ethiopia is not uniform throughout the country. Some studies reported a decreasing trend in seasonal and annual rainfall (Hill and Porter, 2017 ; Asfaw et al., 2018 ). Conversely, other studies have reported increasing trend in annual rainfall (Gemeda, 2019 ; Hundera et al., 2019 ; Tesfamariam et al., 2019 ; Wedajo et al., 2019 ). Other studies found both an increasing and decreasing rainfall trend in different areas (Cheung et al., 2008 ; Omondi et al ., 2014; Eshetu et al., 2018 ; Gebrechorkos et al., 2018 ; Degefie et al., 2019 ). The temporal rainfall attributes, such as the strength of seasonality in rainfall, the onset, cessation, and duration of the rainy season, are extremely relevant for decision-making in the farming system (Mosisa et al. , 2021). Studies already conducted are restricted to analyzing climate change and variability on a broad scale rather than offering in-depth analysis on the scope of the dangers connected to such variability and the potential mitigation strategies. Analysis of rainfall data, such as trends and variability, can serve to provide information for policy and decision makers as well as farmers to enable them create and carry out their plans. Additionally, it aids academics in focusing their research efforts on better adaptation technologies to attain sustainable agricultural productivity in the context of actual conditions. In order to quantify production risks, identify strategies for risk mitigation, and provide comprehensive analyses of rainfall variability, trend, onset date, cessation date, length of growing season, and probability of dry spell occurrence, the present study used historical rainfall records from three districts in the central Gondar zone, northwestern part of Ethiopia. 2. Materials And Methods 2.1. Description of the study area This study was conducted in the Central Gondar Zone (Amhara region), at Alefa and Maksegnit districts). Alefa, Chilga and Gondar zuria districts, North western part of Ethiopia (Fig. 1 and Table 1 ). Table 1 Description of meteorological stations and rainfall database of the three stations used in the analyses Station Geographical Coordination Data Periods Duration of the Data Set Latitude(N) Longitude(E) Altitude(m) Alefa 11.93 36.87 2205 1997–2021 Chilga 12.32 37.03 2150 1985–2021 Maksegnit 12.22 37.37 1950 1987–2021 Alefa district is 162 kilometers southwest of Gondar and 909 kilometers from Addis Abeba, with an average annual temperature of 25 to 30 degrees Celsius and 900 to 1400 millimeters of precipitation. With an altitude of 600–2000 m.a.s.l, a temperature range of 25–42 degrees Celsius, and an average rainfall of 800–1800 mm, the Armacheho district is also located 814 kilometers northwest of Addis Abeba and 65 kilometers north of Gondar town (OAWARD, 2021). One of the districts of Ethiopia's northwestern Amhara region is the Gondar Zuria district. The capital city of Ethiopia, Addis Ababa, is located 700 kilometers to the northwest. At 12.40°N latitude and 37.45°E longitude, this area is situated. It is located between 1550 and 1800 meters above sea level. It receives a mean annual rainfall of 1194 mm and ranges from 711.8 to 1822 mm and mean minimum and maximum temperature ranges from 13°C to 28.2°C (Gondar Zuria DOA, 2021). Chilga District in North Gondar Zone of Amhara Regional State, Ethiopia It is one of the districts in North Gondar Zone and an important stopping point on the historic Gondar-Sudan trade route and is located 61 km west of Gondar town on the way to Metema. The altitude of the Chilga district, which is between 12.55 °N and 37.06 °E, ranges from 900 to 2267 meters above sea level (m.a.s.l). Lowland (900–1500 m.a.s.l.) and midland (1500–2267 m.a.s.l.) agroecology were present. 45% of the soils in the district of Chilga are cambisols, 40% are vertisols, 15% are nitosols (CDOA, 2021). The District experiences between 995 and 1175 mm of annual precipitation and temperatures that range from 11 to 32°C on a daily average (CDFEDO 2021). At the Alefa, Chilga, and Maksegnit sites, the types of soil textures are, in order, clay to heavy clay, sandy clay loam to clay, clay loam to clay, and clay loam to sandy clay loam. Maize (Zea mays L.), sorghum (Sorghum bicolor), teff (Eragrostis teff), and other cereals, pulse and oil crops are produced in the region. 2.2. Data source and methods of analysis The National Meteorological Service Agency (NMSA) of Ethiopia provided daily rainfall data for three stations for the time periods Alefa (1997–2021), Chilga (1985–2021), and Maksegnit (1987–2021). Not more than 15% of the total dataset were missing values. Then, using the Stern et al.-described methodologies, the characterisation concentrated on determining the occurrence of the commencement and cessation of rainfall, the occurrence of dry spells, the length of the growth season, and the variability of seasonal rainfall (Stern et al., 1982 ). The daily rainfall data were examined using the instat program version 3.37. The cumulative deviation approach was used to check the data series for homogeneity, but no heterogeneity was found. Data were produced using the INSTAT plus (v3.37) program in accordance with the first order Markov chain simulation model proposed by Stern and Knock in order to fill in the missing values and reconstruct the gap (Stern et al., 2006 ). The produced data was then examined for physical representations of the relevant stations. The primary reason for selecting this model to replace the missing daily rainfall data is because, as stated by nmsa, it does not overestimate the outcome and provides a more realistic model for each of the research locations (nma, 1996). Additionally, the daily data were compiled into annual, monthly, and seasonal totals using the INSTAT program, and the start and end of the rainy season as well as the length of the growing season were examined (LGP). 2.3. Data quality control Following the days of a year (DOY) entry format, rainfall, minimum, and maximum temperature data were recorded into a Microsoft Excel spreadsheet. The study area's rainfall and temperature records were carefully examined for completeness and temporal consistency as part of the data quality control process. Using first order simulation models of markov chains, missing values in the data series were filled (segel and lamp, 2005; stern et al., 2006 ). This is due to the fact that first-order provides realistic model estimations and doesn't inflate the result. 2.4. Analysis of start, end and length of the growing season To determine when it starts to rain, different authors utilize different threshold values. The criteria employed in this study were the first occurrence of at least 20 mm of precipitation totaled over 3 consecutive days after a specific date and no dry period longer than 9 days in the next 30 days (Stern et al., 2006 ). Similar study likewise used this method, and the earliest start of season (sos) was determined to be the first time that 20 mm of rain fell over the course of three days. The earliest planting date for the study area was chosen as April 1st since the study areas have a monomodal rainfall pattern (long rains from April to September). Accordingly, the first occurrence after April 1st that contains at least 20 mm of rain in a 3-day period with no more than 9 days of dry spell in the ensuing 30 days period was defined as the possible starting date of the growing season. Using a daily evaporation requirement of 5 mm and the soil's ability to store 100 mm of water (Vertisols), the end of the season (EOS) was calculated. It was defined as the first instance of zero soil water following the first week of September. The length of the growth season (LGS), which has a varied maturation duration according on the rainfall regime, is a crucial consideration when choosing the cultivars to be produced. As a result, the length of the growth season (lgs) was defined as the time from the beginning of the rain to its end. It was computed by taking the start date of the rainy season and subtracting it from the end date of the growing season (mupangwa et al., 2011). 2.5. Analysis of probability of occurrence of dry spells The daily rainfall data of (Alefa, Chilga and Maksegnit) were fitted to a simple Markov chain model. Using INSTAT software climate analysis tool on Makrove Chain model; the chance of rain was assessed both on the previous day was dry, i.e., the chance that a dry spell would continue, and also when the previous day was rainy, i.e., the chance that a rainy spell would continue, which is known as a Markov chain. The probability of dry spell lengths of 5, 7 and 10 days during the growing season were determined from the Markov chain model to obtain an overview of dry spell risks during the crop growing season and provide a viable decision aid to various practitioners. Dry spells lengths of 5 to 10 days were selected in order to accommodate both drought sensitive and drought tolerant cultivars during the growing season. 3. Results And Discussion 3.1. Annual Rainfall Variability Indicators of relevant information on temporal rainfall variability over an area include the amount and distribution of annual total precipitation, timing of onset and finish dates, and length of growing seasons (LGS). The annual rainfall total at three sites revealed significant temporal variability, as seen in (Fig. 2 ). At Maksegnit and Alefa sites, the annual rainfall trend was rising, whereas it was falling at Chilga. The Maksegnit site reported the highest and lowest annual rainfall values (17-46.9–828.2 mm), with a coefficient of variation of 19.5, as shown in (Table 2 ). Maksegnit demonstrated greater variation in total yearly rainfall than the other two sites. Many researchers employ various techniques to study climatic variability, with Ethiopia employing PCI and the coefficient of variance the most (Seleshi and Zanke, 2004, Woldeamlak and Conway, 2009). The central rift valley stations were also known to show significant seasonal variability, especially during the major rainy season, while having little yearly rainfall variability, according to numerous academics (Seleshi and Zanke, 2004 and Cheung, et.al, 2008 ). According to Alemayehu and Bewket's (2016) study in the Ethiopian central highlands, crop productivity is significantly impacted by climate variability, which has substantial implications for food scarcity. 3.2. Seasonal rainfall trends The yearly rainfall totals in the current study at three sites are dependent on long-lasting mono-modal rainfall characteristics from March to October (Fig. 3 ). The consistent quantity of rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion. The seasonal variability of rainfall for the kiremt season is depicted in (Table 1 and Fig. 3 ) with the lowest CV values at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. While the variation in the seasonal rainfall totals for Bega and Belg was high—more than 50—at all three sites. Even though the amount of bega and belg rainfall in the study area—3.1 to 8.7 for bega and 8.8 to 11.8 for belg—was relatively small, it had a significant impact on the preparation of the land, the planting of resource-saving long-maturing crop varieties earlier in the season, and had a negative effect when it occurred during harvest. According to a study by Williams and Funk ( 2011 ), rainfall in East Africa has declined over the past three decades. Abebe ( 2017 ) found that different parts of Ethiopia saw high-interannual variation and a decline in average annual rainfall. Woldemlak and Conway ( 2009 ), who studied rainfall data from 12 stations in drought-prone areas of Ethiopia's Amhara Region, and Sisay (2021), who studied three stations in the South Gondar Zone, both came to the same conclusion that Belg rainfall is more variable than Kiremet rainfall. These findings concur with those made by Gemeda ( 2019 ), Cheung et al. ( 2008 ), and Abebe ( 2017 ), which demonstrate the variability of rainfall across Ethiopia's several agro-ecological zones. In that particular region, this variability has a detrimental effect on economic activity from land preparation to harvesting, necessitating extra care from the community. It is crucial to understand how to schedule the onset of seasonal rainfall to the cropping season and the window during which rainfall supplies enough water to meet the crop's water need (Radeny et al., 2019 ). According to (Guido et al., 2020 ), it is crucial for planning and decision-making in rainfed farming systems to have a thorough understanding of the features of seasonal rainfall with regard to the growing season. In bega, the seasonal total rainfall varied from 160.1 to 249.6 mm, in belg, from 302.5 to 387.4 mm, and in kiremt, from 990.2 to 1448.4 mm (Table 2 ). The CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that the total rainfall during the bega and belg seasons is more temporally variable (Table 2 and Fig. 3 ). Table 2 Description of annual and seasonal rainfall totals in the study areas Site total Maximum Mean Minimum StDEV CV % % share Kiremt Bega Belg Alefa 1552.3 1266.7 999.6 107.9 8.5 85.6 5.4 9.0 Chilga 1284.6 1109.5 911.9 123.0 11.1 79.4 8.7 11.8 Maksegnit 1746.6 1199.2 828.2 233.2 19.5 88.0 3.1 8.8 Seasonal totals by site Alefa kiremt 1211.1 1084.1 858 83.32 7.7 Bega 160.1 68.1 12.5 32.31 47.4 belg 387.4 113.6 33.4 84.87 74.73 Chilga kiremt 990.8 881.3 706.1 67.1 7.6 Bega 185.1 96.4 29.5 34.3 35.6 belg 314.2 131.7 15.8 79.8 60.6 Maksegnit kiremt 1448.4 995.97 671.6 178.2 17.9 Bega 249.6 66.16 0 51.8 78.3 belg 302.5 131.1 7.9 65.7 50.1 The research areas see their highest monthly and seasonal rainfall from June through September, as shown in (Fig. 3 ). At the Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. This shows that in the research area, the months of July and August have the most monthly rainfall. Certain data provide insight to consider extra actions like soil water conservation and water harvesting during these months to minimize erosion and maximize water consumption. Similar research has been presented by (Guhathakurta and Saji, 2013 ; Hao et al., 2013 ; Tujuba et al., 2021), which shows that the productivity of the rainfed farming system is determined by the temporal distribution of rainfall with respect to the cropping period in a given hydrological year because this controls the amount of water stored in the soil that is available for biomass production. This result is consistent with data from (FAOSTAT, 2018 ), which was reported for Ethiopia and shows that 95% of agricultural production in Ethiopia is rainfed by smallholders and occurs mostly during the "Meher" lengthy rainy season (April - September). Particularly, the rainy months of June to September (called locally as "Kiremt") are responsible for 65 to 95% of the nation's annual rainfall (Segele and Lamb, 2005 ). Previous research in Ethiopia's Amhara regional state also revealed that the main rainy season, Kuremt, and the short rainy season, Belg, respectively contributed 55–85% and 8–24% of the yearly rainfall totals (Dereje et al., 2012 ; Bewket and Conway, 2009). According to Hadgu et al. (2013), the primary rainy season (Kiremt), which ranges from 50 to 90% depending on the region, contributes significantly to the annual rainfall totals in all stations in northern Ethiopia. The Belg rainfall also adds significantly to the yearly rainfall totals. 3.3. Decadal trends of average monthly rainfall totals The time of change period was investigated using a time series decomposition of long-term rainfall records into decadal bases for monthly totals. In order to see the patterns and the period in which change was seen based on the monthly totals, a 30 year rainfall data (1992–2021) was divided into decadal basis (10 years of period). When compared to the preceding two decadal time periods at the Alefa site, the results shown in (Fig. 5 ) for the near time decade (2012–2021) demonstrated a growing tendency of the months of May, July, August, September, October, and November. With the exception of June and September, all months' rainfall exhibited increasing tendencies in Chilga over the past ten years. At Maksegnit, the pattern is significantly different; it almost always exhibits a declinating tendency over the middle decadal period (2002–2011). On the other side, a rising tendency is visible between the years 1992 to 2001 and 2012 to 2021. (Fig. 5 ). Similar findings have been made on the high to very high monthly concentration of rainfall in the Amhara area of Ethiopia (Bewket and Conway, 2009; Dereje et al., 2012 ). All the stations in northern Ethiopia are classed under high and very high concentration, according to Hadgu et al(2013) .'s assessment, which suggests poor monthly rainfall distribution. 3.4. Onset, End Date and Length of Growing Season Upon the established definition A time series study of the daily rainfall data for a specific area from the prior record using the INSTAT climate guide provides a good image of the potential start, end, and length of the growing season. The length of the growing season and the start and finish of rainfall have a significant impact on agricultural productivity (LGP). Early notice and preparation can benefit from knowing these aspects. Accordingly, the mean onset date ranged from 21-May (142.3 DOY) at Alefa to 12-Jun (164.2 DOY) at Chilga in the study region between (1985 and 2021). (Table 3 ; Fig. 4 ). According to a study done in northern Ethiopia (Tesfaye, 2010), Kiremt growing regions started to appear after the first week of May. While end of season exhibited less variability compared to the other aspects, the lower (25th percentile), median (50th percentile), and upper quartiles (75th percentile) of the rainfall record (Table 3 ) illustrate the existing variability of the onset date, and LGS at all the analyzed sites. The date of the onset of rainfall has lower and higher quartiles that range from 133 (12-May) to 156 (4-Jun) DOY. As a result, planting in Alefa is only permitted once every four years before May 12. On the other hand, Chilga allows planting three times every four years earlier than 4-Jun (156 DOY). In general, it was possible to use the median onset date of 164 DOY (12-Jun) as a reliable planting date for two out of the three sites. The rainy season ends in 12-Oct (286 DOY) once every four years and earlier than 2-Nov (307 DOY) in three of the four years, which is another significant aspect of rainfall (Table 3 ; Fig. 4 ). As a result, at Alefa, Chilga, and Maksegnit, respectively, the rainy season could not last until 3 November (308 DOY), 4 November (309), and 12 November (317 DOY). Crop output is a result of how efficiently resources are used over the course of the growing season. The length of the growing season (LGP) is a crucial aspect of rainfall that should be taken into account from the perspective of crop output. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At the Alefa, Chilga, and Maksegnit sites, there is a 50% chance that the LGS will be less than 140, 137, and 128 days, while there is a 25% chance that it will be longer than 160, 143, and 158 days (Fable 2; Fig. 4 ). Table 3 Statistical description of rainfall features in the study areas Alefa site Rainfall features Min Mean 25% 50% 75% Max SDE cv Start_April 10-Apr 21-May 12-May 16-May 12-Jun 18-Jun 19.3 13.5 Start_May 15-Apr 29-May 13-May 12-Jun 14-Jun 18-Jun 18.84 12.5 End of season 7-Oct 19-Oct 12-Oct 20-Oct 29-Oct 3-Nov 7.83 2.7 LGP 111 143.2 127 140 160 202 22.06 15.4 Chilga site Start_April 2-May 28-May 17-May 27-May 4-Jun 23-Jun 15.98 10.7 Start_May 1-Jun 12-Jun 4-Jun 13-Jun 15-Jun 26-Jun 7.53 4.6 End of season 26-Sep 26-Oct 29-Oct 29-Oct 2-Nov 4-Nov 9.73 3.2 LGP 103 136.5 129 137 143 152 9.67 7.1 Maksegnit site Start_April 15-Apr 5-Jun 15-May 11-Jun 28-Jun 14-Jul 24.56 15.6 Start_May 6-May 8-Jun 17-May 12-Jun 28-Jun 14-Jul 21.86 13.6 End of season 26-Sep 17-Oct 12-Oct 16-Oct 21-Oct 12-Nov 8.47 2.9 LGP 88 133.3 109 128 158 184 27.74 20.8 At the Alefa, Chilga, and Maksegnit sites, the LGS varies from 111 to 202 with a cv of 15.4, from 103 to 152 with a cv of 7.1, and from 88 to 184 with a cv value of 20.8. In comparison to the start of the season and LGP, finish dates exhibited fewer variations across all sites. 3.5. Probability of Dry Spell Length Determine seedling establishment and prospective crop performance at various development stages by using probabilities of dry spell lengths derived during crop growth phases. Higher chance dry spell lengths that occur during crucial crop growth stages are harmful, especially during flowering and grain filling stages. For the research area, the likelihood of dry spells lasting longer than five, seven, and ten days starting in January was calculated (Fig. 7 ). At the Alefa, Chilga, and Maksegnit locations, the likelihood of dry periods lasting more than five days gradually reduces beginning on May 21, October 12, and March 1, and then gradually increases around October 17, October 12, and November 1, respectively (Fig. 7 ). Around 20 May to 2 September at Alefa and Chilga, and 30 April to 20 September at Maksegnit, a 10-day dry spell becomes zero. The presence of a dry spell lasting 10 days confirms the length of the area's growing season. The likelihood of a five-day dry spell begins to decrease on May 5 at Alefa, May 15 at Chilga, and April 10. Starting on June 4 at Alefa and Chilga and May 20 at Maksegnit, the chance level decreases to zero (Fig. 7 ). Depending on the type of crop, the likelihood of experiencing extended dry spells increases quickly starting in the first decade of September (245 DOY), showing the severity of the terminal drought soon following the end of the rains. In this instance, planting must occur before May 10 at Alefa, April 5 at Chilga, and March 1st at Maksegnit. Similar to this, a farmer who is risk averse and unable to decide whether to take on the danger of lengthier dry spells after planting must wait until all dry spell probability reach minimal values (end of May at Alefa and Chilga while 2nd week of May at Maksegnit). JJAS curves showing dry spell likelihood at various lengths during the main season. When rainfall peaks between 4 June and 20 August at Alefa and Chilga, and 20 May to 7 September at Maksegnit, dry spell length converges to its shortest value during those months and then diverges again to indicate the end of the growing season. This implies that standing crops in the study area will be at risk of water shortages after this point. These types of dry spell analysis are crucial for on-farm agricultural decisions like crop or variety selection (short, medium, or long maturing, drought tolerant, or susceptible), as well as crop management techniques, according to (Tesfaye, 2010; Kindie & Walker, 2004 ). (using mulch at seedling stage to cover the soil surface, supplemental irrigation, adjusting fertilizer rate and insecticide application). Additionally, choosing kinds with fill seed in the early stages to avoid moisture deficits in the later stages. However, to make the most of the rainfall amounts during both the belg and kiremt seasons in the research sites, crop varieties that mature in 133–143 days are required. Additionally, these kinds of analysis could offer a summary of each day of the year with different possibilities of dry periods, which could assist farmers in modifying farm management strategies in a specific cropping year. 4. Summary And Conclusion Therefore, it is crucial to comprehend rainfall variability and how it affects local scale in order to assess the situation, design effective adaptation strategies, and lower production risk. As one of the most important weather parameters and one of the most important aspects in rainfed agricultural systems, variability in rainfall is widely acknowledged. Investigating the trend, beginning and ending of the season, the length of the growth season, the likelihood of a dry spell occurring after planting, and determining the risk of planting during the first rain shower are all crucial for this reason. The yearly rainfall totals in the current study at three sites are dependent on long-lasting mono-modal rainfall characteristics from March to October. The consistent quantity of rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion. The lowest CV values for the seasonal fluctuation of rainfall during the kiremt season are 7.7 at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. While the variation in the seasonal rainfall totals for Bega and Belg was high—more than 50—at all three sites. Even though the amount of bega and belg rainfall in the study area—3.1 to 8.7 for bega and 8.8 to 11.8 for belg—was relatively small, it had a significant impact on the preparation of the land, the planting of resource-saving long-maturing crop varieties earlier in the season, and had a negative effect when it occurred during harvest. The CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that there is greater temporal variability in the total rainfall during the bega and belg seasons. The studied areas receive the most annual and monthly precipitation from June through September. At the Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. The average rainy season began on May 21 (142.3 DOY) in Alefa and ended on June 12 (164.2 DOY) in Chilga. However, the rainy season could not last through November 3 (308 DOY), November 4 (309 DOY), and November 12 (317 DOY) in Alefa, Chilga, and Maksegnit, respectively. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At the Alefa, Chilga, and Maksegnit locations, the likelihood of dry periods lasting more than five days gradually reduces beginning on May 21, October 12, and March 1, and then gradually increases again around October 17, October 12, and November 1, respectively. Around 20 May to 2 September at Alefa and Chilga, and 30 April to 20 September at Maksegnit, a 10-day dry spell becomes zero. The presence of a dry spell lasting 10 days confirms the length of the area's growing season. In conclusion, these kinds of analyses could offer a summary of the characteristics of the rainfall in the study area and other places similar to it, which could assist farmers in modifying crop selection, modifying farm management practices, and modifying input utilization in a given cropping year to maximize productivity and minimize loss. Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials : The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Tesfaye Wossen], the first draft of the manuscript was written by [Tesfaye Wossen] and the second, third and fourth authors commented on previous versions Acknowledgments The author acknowledged the National Meteorological Agency of Ethiopia, Bahir Dar Branch for climate data provision and University of Gondar for providing different experimental facilities and technical assistants who supported during field management and follow-ups as well as data collection. Sources of Funding : The author not received specific fund to this research. References Abebe, G. (2017).Long-term Climate Data Descriptions in Ethiopia.Data in Brief.14, pp. 371- 392. DOI: http://doi.org/10.1016/j.dib.2017.07.052 Alefa Development Agriculture office District. Annual report. Maksegnit; 2021. Alefa District Finance and Economy Office, 2021 Alemayehu, A. and Bewket, W. 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Y, and Zanke.U, 2004. “Recent changes in rainfall and rainy days in Ethiopia.” International Journal of Climatology, vol. 24, pp. 973–983, Sisay kelemu Ayehu ,2021. Characterizing historical climate variability and its interconnection with major crops production in the South Gondar Zone, Amhara, Ethiopia. GSJ: Volume 9, Issue 9, September 2021, Online: ISSN 2320-9186 www.globalscientificjournal.com Stern RD, Dennett MD, Dale IC (1982) Analysing daily rainfall measurements to give agronomically useful results. I. Direct methods. Experimental Agriculture 18: 223-236. Stern, R., Rijks, D., Dale, I. and Knock, J., 2006. Instat Climatic Guide. Reading, UK: Statistical Services Centre, Reading University. Tesfamariam, B.G., Gessesse, B., Melgani, F., 2019. Characterizing the spatiotemporal distribution of meteorological drought as a response to climate variability: the case of rift valley lakes basin of Ethiopia. Weather. Clim. Extremes. 26, 100237. Tesfaye Wossen. 2010. Assessment of Spatial and Temporal Variability and Predictability of Rainfall; Implication for Sorghum Production In North Gondar Zone, Ethiopia. MSc. Thesis, Haromaya University, Ethiopia. van Huysen, T., Hansen, J., & Tall, A. (2018). Scaling up climate services for smallholder farmers: Learning from practice. Climate Risk Management, 22, 1–3. https://doi.org/10.1016/j.crm.2018.10.002 Wedajo, G.K., Muleta, M.K., Gessesse, B., Koriche, S.A., 2019. Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia. Environ. Syst. Res. 8, 31. Williams, A.P and Funk, C. (2011) A Westward Extension of the Warm Pool leads to a Westward Extension of the Weaker Circulation, Drying Eastern Africa, Climate Dynamics. 37(11-12), pp. 2417-2435. DOI:https://doi.org/10.1007/s00382-010-0984-y Woldeamlak Bewket, 2009. “Rainfall variability and crop production in Ethiopia case study in the Amhara region” in Proc. ICES,ed,. pp. 823-836 Woldemlak, Bewket. and Conway, Declan. 2009. A note on temporal and spatial variability of rainfall in drought prone Amhara regions of Ethiopia. International Journal of Climatology 27:1467-147 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-2306478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":154805826,"identity":"2a4c0c53-5e5a-4d96-8d9f-d3785fb6a201","order_by":0,"name":"Tesfaye 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18:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2306478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2306478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29675140,"identity":"96f4d10b-ef88-4a6a-9a11-5d9c1f3a0b76","added_by":"auto","created_at":"2022-11-29 17:54:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193048,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/479a56ec3e214c26b8cb447a.png"},{"id":29675138,"identity":"94494dbc-1032-4b6a-9010-3e0435546d4e","added_by":"auto","created_at":"2022-11-29 17:54:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81682,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual rainfall trends at Alefa, Chilga and Maksegnit sites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/d3e6fbd2270caccab7e60a0f.png"},{"id":29675142,"identity":"190ada2e-a1a0-4915-93f1-16b8d7f51eff","added_by":"auto","created_at":"2022-11-29 17:54:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121739,"visible":true,"origin":"","legend":"\u003cp\u003eA long year average seasonal rainfall totals (1985 – 2021)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/956582cea3eeacf5d3329e07.png"},{"id":29675139,"identity":"57930b30-3a3e-4a5c-aa40-1abc48b2c40e","added_by":"auto","created_at":"2022-11-29 17:54:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101738,"visible":true,"origin":"","legend":"\u003cp\u003eThe long year average monthly total rainfall in the study areas\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/0d4b7b8e4aa206e76ab37817.png"},{"id":29676189,"identity":"4401cefc-068f-476c-b404-9485394f29a9","added_by":"auto","created_at":"2022-11-29 18:02:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55090,"visible":true,"origin":"","legend":"\u003cp\u003eA decadal average monthly total rainfall from (1992 – 2021)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/3e24dc4c8b9cb0c88d7a4777.png"},{"id":29676190,"identity":"5e830322-73b0-4db8-9a5b-9426657ed1f5","added_by":"auto","created_at":"2022-11-29 18:02:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144723,"visible":true,"origin":"","legend":"\u003cp\u003eOnset date, end date and LGP at the three districts\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/37c8d4719ce504328de7de4c.png"},{"id":29676575,"identity":"054f38ae-1a50-4883-bc02-957a0c629782","added_by":"auto","created_at":"2022-11-29 18:10:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":114231,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of dry spells longer than 5, 7 and 10 days at Alefa, Chilga \u0026amp; Maksegnit sites starting from January first.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/b9ab82b4e520e3eaa0799403.png"},{"id":46598076,"identity":"27e4ccb8-0c61-47fa-8337-7e04777aad42","added_by":"auto","created_at":"2023-11-17 02:37:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1721709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2306478/v1/7b7e6446-8dad-4fa4-8ddc-1e6341703f59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of rainfall variability and trends for better climate risk management in the major maize producing districts in northwestern part of Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal atmospheric and oceanic circulations are among the factors that contribute to fluctuations in weather variables such as temperature, atmospheric pressure, and rainfall (Endris et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased in the hydrological cycle, variability in amount and distribution of rainfall and occurrence of extreme events in many parts of the globe are intense indications of global climate change and climate variability (Merabtene et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Climate variability and change are major threats for developing countries, especially for the people of sub-Saharan Africas (SSA) (Barana, 2017). As reported by Mirza (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Gemeda and Sima (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), climate change and variability brought back the nations development by reducing crop yield and aggravates food insecurity. In the Sub-Saharan Africa Rainfall anomaly is likely to increase while its amount is expected to decrease (IPCC, 2014).\u003c/p\u003e \u003cp\u003eThe same report explains that, the rising global temperature attributed to the greenhouse gases emission is unequivocal that in turn affects the rainfall anomalies. Higher temperature throughout SSA are causing increased evapotranspiration, shorter growing seasons, drying of the soil, increased pest and disease pressure, shifting in suitability areas for growing crops and livestock, and other direct and indirect impact for agriculture (Cline, 2017; Lobell et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Climate change is also cause increased variability of rainfall in much of SSA, and increased intensity and frequency of extreme events, including droughts, floods and storms. Climate change is a global phenomenon, but its effect is region and location specific; this makes the recent climate studies have been mostly focusing on small scale that provides more detail information for a better management and planning of local resources (Ramli \u003cem\u003eet al\u003c/em\u003e., 2019; Elsanabary and Gan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). SSA including Ethiopia is very vulnerable to climate change and variability as its economy is largely depend on whether sensitive agriculture (Hope, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to (Mekonnen Yibrah et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), agriculture plays a dominant role in the economy of Ethiopia, contributing for national GDP, 80% of the employment and the majority of foreign exchange earnings. In Ethiopia is this sector is highly susceptible to the effect of climate variability with wide gap studies at local level (Degife et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe productivity of the rainfed farming system is determined by the temporal distribution of rainfall with respect to the cropping period in a given hydrological year (Guhathakurta and Saji, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mosisa \u003cem\u003eet al\u003c/em\u003e., 2021) because this controls the amount of water stored in the soil that is available for biomass production. As a result of climate variability, a significant shift in the pattern of rainfall distribution is expected to occur in the coming decades (Beweket, 2009). These shifts in the amount and intensity of rainfall are also projected to affect agricultural productivity, land suitability and welfare levels of households which derive their livelihoods from agriculture. Moreover, rainfall variability affects agriculture through reduced precipitation and increased evapotranspiration as an indirect result of a change in climatic variables other than the direct impacts on temperature and rainfall. Proper understanding of climate variability is vital for better climate risk management in various sectors of the economy and more importantly for agriculture, which is the majority of the community are engaged and their livelihood is highly dependent on it (Mittal \u0026amp; Hariharan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; van Huysen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Assessing climate risk and developing proper tactical and strategic management options in agriculture is impossible without adequate knowledge of climatic conditions acquired through critical analyses of variability and trends in the historical climatic conditions for major agricultural activities at the particular location of interest (Meybeck et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRainfall variability at a global scale over time and space affects all aspects of human activity, especially agricultural economies and social activities (Asmaa \u003cem\u003eet al\u003c/em\u003e., 2020). In particular, rainfall is the most significant meteorological parameter in Ethiopia, as approximately 85% of the Ethiopian labor force is employed in rain-fed agriculture which highly depends on low or high amounts of rainfall availability vital for crop production (Diro et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The trend of rainfall in Ethiopia is not uniform throughout the country. Some studies reported a decreasing trend in seasonal and annual rainfall (Hill and Porter, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, other studies have reported increasing trend in annual rainfall (Gemeda, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hundera et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tesfamariam et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wedajo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other studies found both an increasing and decreasing rainfall trend in different areas (Cheung et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Omondi \u003cem\u003eet al\u003c/em\u003e., 2014; Eshetu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gebrechorkos et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Degefie et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The temporal rainfall attributes, such as the strength of seasonality in rainfall, the onset, cessation, and duration of the rainy season, are extremely relevant for decision-making in the farming system (Mosisa \u003cem\u003eet al.\u003c/em\u003e, 2021).\u003c/p\u003e \u003cp\u003eStudies already conducted are restricted to analyzing climate change and variability on a broad scale rather than offering in-depth analysis on the scope of the dangers connected to such variability and the potential mitigation strategies. Analysis of rainfall data, such as trends and variability, can serve to provide information for policy and decision makers as well as farmers to enable them create and carry out their plans. Additionally, it aids academics in focusing their research efforts on better adaptation technologies to attain sustainable agricultural productivity in the context of actual conditions. In order to quantify production risks, identify strategies for risk mitigation, and provide comprehensive analyses of rainfall variability, trend, onset date, cessation date, length of growing season, and probability of dry spell occurrence, the present study used historical rainfall records from three districts in the central Gondar zone, northwestern part of Ethiopia.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Central Gondar Zone (Amhara region), at Alefa and Maksegnit districts). Alefa, Chilga and Gondar zuria districts, North western part of Ethiopia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eDescription of meteorological stations and rainfall database of the three stations used in the analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGeographical Coordination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eData Periods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration of the Data Set\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude(E)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltitude(m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlefa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1997\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChilga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1985\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaksegnit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1987\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlefa district is 162 kilometers southwest of Gondar and 909 kilometers from Addis Abeba, with an average annual temperature of 25 to 30 degrees Celsius and 900 to 1400 millimeters of precipitation. With an altitude of 600\u0026ndash;2000 m.a.s.l, a temperature range of 25\u0026ndash;42 degrees Celsius, and an average rainfall of 800\u0026ndash;1800 mm, the Armacheho district is also located 814 kilometers northwest of Addis Abeba and 65 kilometers north of Gondar town (OAWARD, 2021). One of the districts of Ethiopia's northwestern Amhara region is the Gondar Zuria district. The capital city of Ethiopia, Addis Ababa, is located 700 kilometers to the northwest. At 12.40\u0026deg;N latitude and 37.45\u0026deg;E longitude, this area is situated. It is located between 1550 and 1800 meters above sea level. It receives a mean annual rainfall of 1194 mm and ranges from 711.8 to 1822 mm and mean minimum and maximum temperature ranges from 13\u0026deg;C to 28.2\u0026deg;C (Gondar Zuria DOA, 2021). Chilga District in North Gondar Zone of Amhara Regional State, Ethiopia It is one of the districts in North Gondar Zone and an important stopping point on the historic Gondar-Sudan trade route and is located 61 km west of Gondar town on the way to Metema.\u003c/p\u003e \u003cp\u003eThe altitude of the Chilga district, which is between 12.55 \u0026deg;N and 37.06 \u0026deg;E, ranges from 900 to 2267 meters above sea level (m.a.s.l). Lowland (900\u0026ndash;1500 m.a.s.l.) and midland (1500\u0026ndash;2267 m.a.s.l.) agroecology were present. 45% of the soils in the district of Chilga are cambisols, 40% are vertisols, 15% are nitosols (CDOA, 2021). The District experiences between 995 and 1175 mm of annual precipitation and temperatures that range from 11 to 32\u0026deg;C on a daily average (CDFEDO 2021). At the Alefa, Chilga, and Maksegnit sites, the types of soil textures are, in order, clay to heavy clay, sandy clay loam to clay, clay loam to clay, and clay loam to sandy clay loam. Maize (Zea mays L.), sorghum (Sorghum bicolor), teff (Eragrostis teff), and other cereals, pulse and oil crops are produced in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data source and methods of analysis\u003c/h2\u003e \u003cp\u003eThe National Meteorological Service Agency (NMSA) of Ethiopia provided daily rainfall data for three stations for the time periods Alefa (1997\u0026ndash;2021), Chilga (1985\u0026ndash;2021), and Maksegnit (1987\u0026ndash;2021). Not more than 15% of the total dataset were missing values. Then, using the Stern et al.-described methodologies, the characterisation concentrated on determining the occurrence of the commencement and cessation of rainfall, the occurrence of dry spells, the length of the growth season, and the variability of seasonal rainfall (Stern et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The daily rainfall data were examined using the instat program version 3.37. The cumulative deviation approach was used to check the data series for homogeneity, but no heterogeneity was found. Data were produced using the INSTAT plus (v3.37) program in accordance with the first order Markov chain simulation model proposed by Stern and Knock in order to fill in the missing values and reconstruct the gap (Stern et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The produced data was then examined for physical representations of the relevant stations. The primary reason for selecting this model to replace the missing daily rainfall data is because, as stated by nmsa, it does not overestimate the outcome and provides a more realistic model for each of the research locations (nma, 1996). Additionally, the daily data were compiled into annual, monthly, and seasonal totals using the INSTAT program, and the start and end of the rainy season as well as the length of the growing season were examined (LGP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data quality control\u003c/h2\u003e \u003cp\u003eFollowing the days of a year (DOY) entry format, rainfall, minimum, and maximum temperature data were recorded into a Microsoft Excel spreadsheet. The study area's rainfall and temperature records were carefully examined for completeness and temporal consistency as part of the data quality control process. Using first order simulation models of markov chains, missing values in the data series were filled (segel and lamp, 2005; stern et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This is due to the fact that first-order provides realistic model estimations and doesn't inflate the result.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis of start, end and length of the growing season\u003c/h2\u003e \u003cp\u003eTo determine when it starts to rain, different authors utilize different threshold values. The criteria employed in this study were the first occurrence of at least 20 mm of precipitation totaled over 3 consecutive days after a specific date and no dry period longer than 9 days in the next 30 days (Stern et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Similar study likewise used this method, and the earliest start of season (sos) was determined to be the first time that 20 mm of rain fell over the course of three days. The earliest planting date for the study area was chosen as April 1st since the study areas have a monomodal rainfall pattern (long rains from April to September). Accordingly, the first occurrence after April 1st that contains at least 20 mm of rain in a 3-day period with no more than 9 days of dry spell in the ensuing 30 days period was defined as the possible starting date of the growing season.\u003c/p\u003e \u003cp\u003eUsing a daily evaporation requirement of 5 mm and the soil's ability to store 100 mm of water (Vertisols), the end of the season (EOS) was calculated. It was defined as the first instance of zero soil water following the first week of September. The length of the growth season (LGS), which has a varied maturation duration according on the rainfall regime, is a crucial consideration when choosing the cultivars to be produced. As a result, the length of the growth season (lgs) was defined as the time from the beginning of the rain to its end. It was computed by taking the start date of the rainy season and subtracting it from the end date of the growing season (mupangwa et al., 2011).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Analysis of probability of occurrence of dry spells\u003c/h2\u003e \u003cp\u003eThe daily rainfall data of (Alefa, Chilga and Maksegnit) were fitted to a simple Markov chain model. Using INSTAT software climate analysis tool on Makrove Chain model; the chance of rain was assessed both on the previous day was dry, i.e., the chance that a dry spell would continue, and also when the previous day was rainy, i.e., the chance that a rainy spell would continue, which is known as a Markov chain. The probability of dry spell lengths of 5, 7 and 10 days during the growing season were determined from the Markov chain model to obtain an overview of dry spell risks during the crop growing season and provide a viable decision aid to various practitioners. Dry spells lengths of 5 to 10 days were selected in order to accommodate both drought sensitive and drought tolerant cultivars during the growing season.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Annual Rainfall Variability\u003c/h2\u003e \u003cp\u003eIndicators of relevant information on temporal rainfall variability over an area include the amount and distribution of annual total precipitation, timing of onset and finish dates, and length of growing seasons (LGS). The annual rainfall total at three sites revealed significant temporal variability, as seen in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At Maksegnit and Alefa sites, the annual rainfall trend was rising, whereas it was falling at Chilga. The Maksegnit site reported the highest and lowest annual rainfall values (17-46.9\u0026ndash;828.2 mm), with a coefficient of variation of 19.5, as shown in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Maksegnit demonstrated greater variation in total yearly rainfall than the other two sites. Many researchers employ various techniques to study climatic variability, with Ethiopia employing PCI and the coefficient of variance the most (Seleshi and Zanke, 2004, Woldeamlak and Conway, 2009). The central rift valley stations were also known to show significant seasonal variability, especially during the major rainy season, while having little yearly rainfall variability, according to numerous academics (Seleshi and Zanke, 2004 and Cheung, et.al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). According to Alemayehu and Bewket's (2016) study in the Ethiopian central highlands, crop productivity is significantly impacted by climate variability, which has substantial implications for food scarcity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Seasonal rainfall trends\u003c/h2\u003e \u003cp\u003eThe yearly rainfall totals in the current study at three sites are dependent on long-lasting mono-modal rainfall characteristics from March to October (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The consistent quantity of rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion. The seasonal variability of rainfall for the kiremt season is depicted in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with the lowest CV values at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. While the variation in the seasonal rainfall totals for Bega and Belg was high\u0026mdash;more than 50\u0026mdash;at all three sites. Even though the amount of bega and belg rainfall in the study area\u0026mdash;3.1 to 8.7 for bega and 8.8 to 11.8 for belg\u0026mdash;was relatively small, it had a significant impact on the preparation of the land, the planting of resource-saving long-maturing crop varieties earlier in the season, and had a negative effect when it occurred during harvest.\u003c/p\u003e \u003cp\u003eAccording to a study by Williams and Funk (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), rainfall in East Africa has declined over the past three decades. Abebe (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that different parts of Ethiopia saw high-interannual variation and a decline in average annual rainfall.\u003c/p\u003e \u003cp\u003eWoldemlak and Conway (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), who studied rainfall data from 12 stations in drought-prone areas of Ethiopia's Amhara Region, and Sisay (2021), who studied three stations in the South Gondar Zone, both came to the same conclusion that Belg rainfall is more variable than Kiremet rainfall. These findings concur with those made by Gemeda (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Cheung et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and Abebe (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which demonstrate the variability of rainfall across Ethiopia's several agro-ecological zones. In that particular region, this variability has a detrimental effect on economic activity from land preparation to harvesting, necessitating extra care from the community.\u003c/p\u003e \u003cp\u003eIt is crucial to understand how to schedule the onset of seasonal rainfall to the cropping season and the window during which rainfall supplies enough water to meet the crop's water need (Radeny et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to (Guido et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it is crucial for planning and decision-making in rainfed farming systems to have a thorough understanding of the features of seasonal rainfall with regard to the growing season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn bega, the seasonal total rainfall varied from 160.1 to 249.6 mm, in belg, from 302.5 to 387.4 mm, and in kiremt, from 990.2 to 1448.4 mm (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that the total rainfall during the bega and belg seasons is more temporally variable (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\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\u003eDescription of annual and seasonal rainfall totals in the study areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSite total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStDEV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e% share\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eKiremt\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eBega\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eBelg\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlefa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1552.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1266.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e999.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChilga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1284.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1109.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e911.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaksegnit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1199.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e828.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e233.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eSeasonal totals by site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAlefa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekiremt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1211.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1084.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebelg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChilga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekiremt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e990.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e881.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e706.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebelg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMaksegnit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekiremt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1448.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e995.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e671.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebelg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe research areas see their highest monthly and seasonal rainfall from June through September, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. This shows that in the research area, the months of July and August have the most monthly rainfall. Certain data provide insight to consider extra actions like soil water conservation and water harvesting during these months to minimize erosion and maximize water consumption. Similar research has been presented by (Guhathakurta and Saji, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tujuba et al., 2021), which shows that the productivity of the rainfed farming system is determined by the temporal distribution of rainfall with respect to the cropping period in a given hydrological year because this controls the amount of water stored in the soil that is available for biomass production. This result is consistent with data from (FAOSTAT, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which was reported for Ethiopia and shows that 95% of agricultural production in Ethiopia is rainfed by smallholders and occurs mostly during the \"Meher\" lengthy rainy season (April - September). Particularly, the rainy months of June to September (called locally as \"Kiremt\") are responsible for 65 to 95% of the nation's annual rainfall (Segele and Lamb, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research in Ethiopia's Amhara regional state also revealed that the main rainy season, Kuremt, and the short rainy season, Belg, respectively contributed 55\u0026ndash;85% and 8\u0026ndash;24% of the yearly rainfall totals (Dereje et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bewket and Conway, 2009). According to Hadgu et al. (2013), the primary rainy season (Kiremt), which ranges from 50 to 90% depending on the region, contributes significantly to the annual rainfall totals in all stations in northern Ethiopia. The Belg rainfall also adds significantly to the yearly rainfall totals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Decadal trends of average monthly rainfall totals\u003c/h2\u003e \u003cp\u003eThe time of change period was investigated using a time series decomposition of long-term rainfall records into decadal bases for monthly totals. In order to see the patterns and the period in which change was seen based on the monthly totals, a 30 year rainfall data (1992\u0026ndash;2021) was divided into decadal basis (10 years of period). When compared to the preceding two decadal time periods at the Alefa site, the results shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) for the near time decade (2012\u0026ndash;2021) demonstrated a growing tendency of the months of May, July, August, September, October, and November. With the exception of June and September, all months' rainfall exhibited increasing tendencies in Chilga over the past ten years. At Maksegnit, the pattern is significantly different; it almost always exhibits a declinating tendency over the middle decadal period (2002\u0026ndash;2011). On the other side, a rising tendency is visible between the years 1992 to 2001 and 2012 to 2021. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar findings have been made on the high to very high monthly concentration of rainfall in the Amhara area of Ethiopia (Bewket and Conway, 2009; Dereje et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All the stations in northern Ethiopia are classed under high and very high concentration, according to Hadgu et al(2013) .'s assessment, which suggests poor monthly rainfall distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Onset, End Date and Length of Growing Season\u003c/h2\u003e \u003cp\u003eUpon the established definition A time series study of the daily rainfall data for a specific area from the prior record using the INSTAT climate guide provides a good image of the potential start, end, and length of the growing season. The length of the growing season and the start and finish of rainfall have a significant impact on agricultural productivity (LGP). Early notice and preparation can benefit from knowing these aspects. Accordingly, the mean onset date ranged from 21-May (142.3 DOY) at Alefa to 12-Jun (164.2 DOY) at Chilga in the study region between (1985 and 2021). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to a study done in northern Ethiopia (Tesfaye, 2010), Kiremt growing regions started to appear after the first week of May.\u003c/p\u003e \u003cp\u003eWhile end of season exhibited less variability compared to the other aspects, the lower (25th percentile), median (50th percentile), and upper quartiles (75th percentile) of the rainfall record (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) illustrate the existing variability of the onset date, and LGS at all the analyzed sites. The date of the onset of rainfall has lower and higher quartiles that range from 133 (12-May) to 156 (4-Jun) DOY. As a result, planting in Alefa is only permitted once every four years before May 12. On the other hand, Chilga allows planting three times every four years earlier than 4-Jun (156 DOY). In general, it was possible to use the median onset date of 164 DOY (12-Jun) as a reliable planting date for two out of the three sites.\u003c/p\u003e \u003cp\u003eThe rainy season ends in 12-Oct (286 DOY) once every four years and earlier than 2-Nov (307 DOY) in three of the four years, which is another significant aspect of rainfall (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As a result, at Alefa, Chilga, and Maksegnit, respectively, the rainy season could not last until 3 November (308 DOY), 4 November (309), and 12 November (317 DOY). Crop output is a result of how efficiently resources are used over the course of the growing season. The length of the growing season (LGP) is a crucial aspect of rainfall that should be taken into account from the perspective of crop output. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At the Alefa, Chilga, and Maksegnit sites, there is a 50% chance that the LGS will be less than 140, 137, and 128 days, while there is a 25% chance that it will be longer than 160, 143, and 158 days (Fable 2; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical description of rainfall features in the study areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eAlefa site\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ecv\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_April\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10-Apr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15-Apr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e29-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3-Nov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e143.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eChilga site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_April\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e28-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e26-Sep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e26-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2-Nov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4-Nov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e136.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eMaksegnit site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_April\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15-Apr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14-Jul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart_May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17-May\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28-Jun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14-Jul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e26-Sep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e17-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12-Nov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e133.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.8\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\u003e \u003c/p\u003e \u003cp\u003eAt the Alefa, Chilga, and Maksegnit sites, the LGS varies from 111 to 202 with a cv of 15.4, from 103 to 152 with a cv of 7.1, and from 88 to 184 with a cv value of 20.8. In comparison to the start of the season and LGP, finish dates exhibited fewer variations across all sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Probability of Dry Spell Length\u003c/h2\u003e \u003cp\u003eDetermine seedling establishment and prospective crop performance at various development stages by using probabilities of dry spell lengths derived during crop growth phases. Higher chance dry spell lengths that occur during crucial crop growth stages are harmful, especially during flowering and grain filling stages. For the research area, the likelihood of dry spells lasting longer than five, seven, and ten days starting in January was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). At the Alefa, Chilga, and Maksegnit locations, the likelihood of dry periods lasting more than five days gradually reduces beginning on May 21, October 12, and March 1, and then gradually increases around October 17, October 12, and November 1, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Around 20 May to 2 September at Alefa and Chilga, and 30 April to 20 September at Maksegnit, a 10-day dry spell becomes zero. The presence of a dry spell lasting 10 days confirms the length of the area's growing season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe likelihood of a five-day dry spell begins to decrease on May 5 at Alefa, May 15 at Chilga, and April 10. Starting on June 4 at Alefa and Chilga and May 20 at Maksegnit, the chance level decreases to zero (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDepending on the type of crop, the likelihood of experiencing extended dry spells increases quickly starting in the first decade of September (245 DOY), showing the severity of the terminal drought soon following the end of the rains. In this instance, planting must occur before May 10 at Alefa, April 5 at Chilga, and March 1st at Maksegnit. Similar to this, a farmer who is risk averse and unable to decide whether to take on the danger of lengthier dry spells after planting must wait until all dry spell probability reach minimal values (end of May at Alefa and Chilga while 2nd week of May at Maksegnit).\u003c/p\u003e \u003cp\u003eJJAS curves showing dry spell likelihood at various lengths during the main season. When rainfall peaks between 4 June and 20 August at Alefa and Chilga, and 20 May to 7 September at Maksegnit, dry spell length converges to its shortest value during those months and then diverges again to indicate the end of the growing season. This implies that standing crops in the study area will be at risk of water shortages after this point.\u003c/p\u003e \u003cp\u003eThese types of dry spell analysis are crucial for on-farm agricultural decisions like crop or variety selection (short, medium, or long maturing, drought tolerant, or susceptible), as well as crop management techniques, according to (Tesfaye, 2010; Kindie \u0026amp; Walker, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). (using mulch at seedling stage to cover the soil surface, supplemental irrigation, adjusting fertilizer rate and insecticide application). Additionally, choosing kinds with fill seed in the early stages to avoid moisture deficits in the later stages. However, to make the most of the rainfall amounts during both the belg and kiremt seasons in the research sites, crop varieties that mature in 133\u0026ndash;143 days are required. Additionally, these kinds of analysis could offer a summary of each day of the year with different possibilities of dry periods, which could assist farmers in modifying farm management strategies in a specific cropping year.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Summary And Conclusion","content":"\u003cp\u003eTherefore, it is crucial to comprehend rainfall variability and how it affects local scale in order to assess the situation, design effective adaptation strategies, and lower production risk. As one of the most important weather parameters and one of the most important aspects in rainfed agricultural systems, variability in rainfall is widely acknowledged. Investigating the trend, beginning and ending of the season, the length of the growth season, the likelihood of a dry spell occurring after planting, and determining the risk of planting during the first rain shower are all crucial for this reason. The yearly rainfall totals in the current study at three sites are dependent on long-lasting mono-modal rainfall characteristics from March to October. The consistent quantity of rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion.\u003c/p\u003e \u003cp\u003eThe lowest CV values for the seasonal fluctuation of rainfall during the kiremt season are 7.7 at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. While the variation in the seasonal rainfall totals for Bega and Belg was high\u0026mdash;more than 50\u0026mdash;at all three sites. Even though the amount of bega and belg rainfall in the study area\u0026mdash;3.1 to 8.7 for bega and 8.8 to 11.8 for belg\u0026mdash;was relatively small, it had a significant impact on the preparation of the land, the planting of resource-saving long-maturing crop varieties earlier in the season, and had a negative effect when it occurred during harvest.\u003c/p\u003e \u003cp\u003eThe CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that there is greater temporal variability in the total rainfall during the bega and belg seasons. The studied areas receive the most annual and monthly precipitation from June through September. At the Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. The average rainy season began on May 21 (142.3 DOY) in Alefa and ended on June 12 (164.2 DOY) in Chilga. However, the rainy season could not last through November 3 (308 DOY), November 4 (309 DOY), and November 12 (317 DOY) in Alefa, Chilga, and Maksegnit, respectively. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At the Alefa, Chilga, and Maksegnit locations, the likelihood of dry periods lasting more than five days gradually reduces beginning on May 21, October 12, and March 1, and then gradually increases again around October 17, October 12, and November 1, respectively. Around 20 May to 2 September at Alefa and Chilga, and 30 April to 20 September at Maksegnit, a 10-day dry spell becomes zero. The presence of a dry spell lasting 10 days confirms the length of the area's growing season. In conclusion, these kinds of analyses could offer a summary of the characteristics of the rainfall in the study area and other places similar to it, which could assist farmers in modifying crop selection, modifying farm management practices, and modifying input utilization in a given cropping year to maximize productivity and minimize loss.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent for publication:\u003c/strong\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u0026nbsp;The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Tesfaye Wossen], the first draft of the manuscript was written by [Tesfaye Wossen] and the second, third and fourth authors commented on previous versions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author acknowledged the National Meteorological Agency of Ethiopia, Bahir Dar Branch for climate data provision and University of Gondar for providing different experimental facilities and technical assistants who supported during field management and follow-ups as well as data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e: The author not received specific fund to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebe, G. (2017).Long-term Climate Data Descriptions in Ethiopia.Data in Brief.14, pp. 371- 392. 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Climate Risk Management, 22, 1\u0026ndash;3. https://doi.org/10.1016/j.crm.2018.10.002\u003c/li\u003e\n\u003cli\u003eWedajo, G.K., Muleta, M.K., Gessesse, B., Koriche, S.A., 2019. Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia. Environ. Syst. Res. 8, 31. \u003c/li\u003e\n\u003cli\u003eWilliams, A.P and Funk, C. (2011) A Westward Extension of the Warm Pool leads to a Westward Extension of the Weaker Circulation, Drying Eastern Africa, Climate Dynamics. 37(11-12), pp. 2417-2435. DOI:https://doi.org/10.1007/s00382-010-0984-y\u003c/li\u003e\n\u003cli\u003eWoldeamlak Bewket, 2009. \u0026ldquo;Rainfall variability and crop production in Ethiopia case study in the Amhara region\u0026rdquo; in Proc. ICES,ed,. pp. 823-836 \u003c/li\u003e\n\u003cli\u003eWoldemlak, Bewket. and Conway, Declan. 2009. A note on temporal and spatial variability of rainfall in drought prone Amhara regions of Ethiopia. International Journal of Climatology 27:1467-147\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rainfall, Variability, onset, cessation and dry spell","lastPublishedDoi":"10.21203/rs.3.rs-2306478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2306478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current study evaluated historical rainfall data for its variability in three districts of the Central Gondar Zone in Ethiopia's northwestern region. The rainfall required for crop production in the research areas is the contribution of rain from June to September (kiremt rain). The annual rainfall total has a higher percentage during the Kiremt season, ranging from 79% at Chilga, 85.6% at Alefa, and 88% at Maksegnit. Rainfall totals from the bega (October to January) and belg (February to May) seasons made up the remaining portion. The lowest CV values for the seasonal fluctuation of rainfall during the kiremt season are 7.7 at Alefa, 7.6 at Chilga, and 17.9 at Maksegnit. The CV is substantially larger for the total rainfall during the bega and belg seasons than it is for the kiremt season, indicating that there is greater temporal variability in the total rainfall during the bega and belg seasons. At Alefa and Chilga locations, the monthly totals were 280 mm and 357 mm respectively in July, while the Maksegnit site recorded 349 mm in August. The average rainy season began on May 21 (142.3 DOY) in Alefa and ended on June 12 (164.2 DOY) in Chilga. On the other hand, the rainy season ends November 3 (308 DOY), November 4 (309 DOY), and November 12 (317 DOY) in Alefa, Chilga, and Maksegnit, respectively. At Maksegnit, Chilga, and Alefa, the mean LGP is 133.3, 136.5, and 143.2, respectively. At Alefa, the likelihood of dry periods lasting more than five days steadily reduces starting on May 21, October 12, and March 1, and then gradually increases again around October 17 and November 1.Therfore, this findings give a clue of understanding the rainfall features and associated to crop production in the study area.\u003c/p\u003e","manuscriptTitle":"Analysis of rainfall variability and trends for better climate risk management in the major maize producing districts in northwestern part of Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-29 17:54:13","doi":"10.21203/rs.3.rs-2306478/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a3ca279-7dde-432f-bf8b-9d1e187e7ddd","owner":[],"postedDate":"November 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-17T02:29:35+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-29 17:54:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2306478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2306478","identity":"rs-2306478","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}