Journal of Climatology & Weather Forecasting

Analysis of Rainfall Variability and Trends for Better Climate Risk Management in the Major Maize Producing Districts in Northwestern Part of Ethiopia

Tesfaye Wossen Dejenie^1*, Kindie Tesfaye², Belay Simane³ and Yenus Ousman^{1 *}Correspondence: Tesfaye Wossen Dejenie, University of Gondar, College of Agriculture and Environmental Sciences, Ethiopia, Tel: +91 781780414, Email:

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Introduction

lobal atmospheric and oceanic circulations are among the factors that contribute to fluctuations in weather variables such as temperature, atmospheric pressure, and rainfall [1]. 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 indicaions of global climate change and climate variability [2]. Climate variability and change are major threats for developing countries, especially for the people of Sub-Saharan Africas (SSA). 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 [3,4].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 [5-8]. 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 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[3,4]. In Ethiopia is this sector is highly susceptible to the effect of climate variability with wide gap studies at local level [9,10].

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 [6,11,12,]. As a result of climate variability, a significant shift in the pattern of rainfall distribution is expected to occur in the coming decades [13]. 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 [3, 4, 14-16]. 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 [13].

Rainfall variability at a global scale over time and space affects all aspects of human activity, especially agricultural economies and social activities [17]. 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 [15]. The trend of rainfall in Ethiopia is not uniform throughout the country. Some studies reported a decreasing trend in seasonal and annual rainfall [16,17]. Conversely, other studies have reported increasing trend in annual rainfall [17-20]. Other studies found both an increasing and decreasing rainfall trend in different areas [14, 17, 5, 21-23]. 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 [24].

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[25,26].

Material and Methods

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 (Figure 1 and Table 1).

Figure 1: Study area map

Table 1. Description of meteorological stations and rainfall database of the three stations used in the analyses

Alefa district is 162 km southwest of Gondar and 909 kmfrom Addis Abeba, with an average annual temperature of 25°C to 30°C and 900 mm to 1400 mm of precipitation. With an altitude of 600 m.a.s.l-2000 m.a.s.l, a temperature range of 25°C-42°C, 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. 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 m and 1800 m above sea level. It receives a mean annual rainfall of 1194 mm and ranges from 711.8 mm to 1822 mm and mean minimum and maximum temperature ranges from 13°C to 28.2°C. 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 meters to 2267 meters above sea level (m.a.s.l). Lowland (900 m.a.s.l-1500 m.a.s.l.) and midland (1500 m.a.s.l-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 mm 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.

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 [27]. 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 [27]. 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).

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 [26, 28]. This is due to the fact that first-order provides realistic model estimations and doesn't inflate the result.

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. 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 [28,29].

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 days, 7 days 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 days to 10 days were selected in order to accommodate both drought sensitive and drought tolerant cultivars during the growing season.

Results and Discussion

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 (Figure 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 mm-46.9 mm-828.2 mm), with a coefficient of variation of 19.5, as shown in Table 1. 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 [26,29-31]. 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 [32-34]. 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.

Figure 2: . Annual rainfall trends at Alefa, Chilga and Maksegnit sites.

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 (Figure 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 Figure 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 resourcesaving long-maturing crop varieties earlier in the season, and had a negative effect when it occurred during harvest.

Figure 3: A long year average seasonal rainfall totals (1985–2021).

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 [35].

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 [36-38] .

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 [39]. 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 mm to 249.6 mm, in belg, from 302.5 mm to 387.4 mm, and in kiremt, from 990.2 mm 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 Figure 3).

Table 2. Description of annual and seasonal rainfall totals in the study areas.

The research areas see their highest monthly and seasonal rainfall from June through September, as shown in (Figure 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 [38-41]. 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 [26, 29, 42].

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. 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.

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 (Figure 4) 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 (Figure 4).

Figure 4: A decadal average monthly total rainfall from (1992 – 2021).

Similar findings have been made on the high to very high monthly concentration of rainfall in the Amhara area of Ethiopia [43-46, 33, 39]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.

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, Figure 4). According to a study done in northern Ethiopia (Tesfaye, 2010), Kiremt growing regions started to appear after the first week of May [47,48].

Table 3. Statistical description of rainfall features in the study areas

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, Figure 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 days, 137 days, and 128 days, while there is a 25% chance that it will be longer than 160 days, 143 days, and 158 days (Table 2, Figure 5) [49,50].

Figure 5: The long year average monthly total rainfall in the study areas

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 (Figure 6)

Figure 6: Onset date, end date and LGP at the three districts.

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 (Figure 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 (Figure 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.

Figure 7:Probability of dry spells longer than 5 days, 7 days and 10 days at Alefa, Chilga & Maksegnit sites starting from January first.

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 (Figure 7) [51-53].

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 2^nd week of May at Maksegnit) [54].

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) [55,56]. 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 days-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.

Conclusion and Summary

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 for bega to 8.7 for bega and 8.8 for bega 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..

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 of the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Acknowledgement

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.

References

1. Seid, H. et al. "Future changes in rainfall associated with ENSO, IOD and changes in the mean state over Eastern Africa." Climate dynamics 52.3 (2019): 2029-2053.

   [GoogleScholar] [CrossRef]

2. Tarek,M. et al. "Assessment of seasonal and annual rainfall trends and variability in Sharjah City, UAE." Advan Meteorology 2016 (2016).

   [GoogleScholar] [CrossRef]

3. William R. Global warming and agriculture: End-of-century estimates by country. Peterson Institute, 2007.

   [GoogleScholar]

4. Alefa Development Agriculture office District. Annual report. Maksegnit; 2021.
5. Alefa District Finance and Economy Office, 2021.

   [GoogleScholar] [CrossRef]

6. David B., et al. "Prioritizing climate change adaptation needs for food security in 2030." Science 319.5863 (2008): 607-610.

   [GoogleScholar] [CrossRef]

7. Elsanabary, et al. "Evaluation of climate anomalies impacts on the Upper Blue Nile Basin in Ethiopia using a distributed and a lumped hydrologic model." J Hydro 530 (2015): 225-240..

   [GoogleScholar] [CrossRef]

8. Hope Sr, and Kempe R. "Climate change and poverty in Africa." Int J Sustainable Dev World Ecology 16.6 (2009): 451-461.

   [GoogleScholar] [CrossRef]

9. Mekonnen,Y., et al. "Assessing rainfall variability at Adwa District, Central Tigray, Ethiopia." Int J Res Enviro Sci 5.1 (2019): 1-5.

   [GoogleScholar] [CrossRef]

10. Degife, et al. "Climate change impacts on potential maize yields in Gambella region, Ethiopia." Reg Environmental Change 21.2 (2021): 1-12.

    [GoogleScholar] [CrossRef]

11.  Pulak, G, and Saji, E. "Detecting changes in rainfall pattern and seasonality index vis-à-vis increasing water scarcity in Maharashtra." J Earth System Sci 122.3 (2013): 639-649.

    [GoogleScholar] [CrossRef]

12. Surabhi,M and  Hariharan V,. "Mobile-based climate services impact on farmers risk management ability in India." Climate Risk Management 22 (2018): 42-51.

    [GoogleScholar] [CrossRef]

13. Meybeck, A. "Food and Agriculture Organization of the United Nations, & Organisation for Economic Co-operation and Development (Eds.). 2012. Building resilience for adaptation to climate change in the agriculture sector: Proceedings of a Joint FAO." OECD Workshop, 2012.
14. Asmaa, A., et al. "Seasonal rainfall variability in Ethiopia and its long-term link to global sea surface temperatures." Water 12.1 (2019): 55.

    [GoogleScholar] [CrossRef]

15. Diro, G. T., et al. "Teleconnections between Ethiopian summer rainfall and sea surface temperature: part I—observation and modelling." Climate dynamics 37.1 (2011): 103-119.

    [GoogleScholar] [CrossRef]

16. Ruth V, et al. "Vulnerability to drought and food price shocks: evidence from Ethiopia." World Development 96 (2017): 65-77.

    [GoogleScholar] [CrossRef]

17. Gemeda, D. O. "Climate change variability analysis in and around Jinka, southern Ethiopia. With special emphasis on temperature and rainfall." (2019).

    [GoogleScholar] [CrossRef]

18. Gebrehiwot B, et al. "Characterizing the spatiotemporal distribution of meteorological drought as a response to climate variability: The case of rift valley lakes basin of Ethiopia." Weath Climate Extremes 26 (2019): 100237.

    [GoogleScholar] [CrossRef]

19. Gizachew K, et al. "Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia." Envi Systems Res 8.1 (2019): 1-24.

    [GoogleScholar] [CrossRef]

20. Wing H., et al. "Trends and spatial distribution of annual and seasonal rainfall in Ethiopia." Int J Climatology: A J Royal Meteoro Socie 28.13 (2008): 1723-1734.

    [GoogleScholar] [CrossRef]

21. Arragaw, et al. "Trees and rural households’ adaptation to Local environmental change in the central highlands of Ethiopia." J Land Use Science 13.1-2 (2018): 130-145.

    [GoogleScholar] [CrossRef]

22. Gezahegn, A. "Long-term climate data description in Ethiopia" Data in brief 14 (2017): 371-392..

    [GoogleScholar] [CrossRef]

23. Girma, et al. "Climate variability and small-scale farmer adaptation strategy in Setema-Gatira area of Jimmaa, southwestern Ethiopia." Ame J biological envir stat (2018).

    [GoogleScholar] [CrossRef]

24. Hailu, S et al. "Evaluation of multiple climate data sources for managing environmental resources in East Africa." Hydro Earth System Sci 22.8 (2018): 4547-4564.

    [GoogleScholar] [CrossRef]

25. Mekonnen Y, et al. "Assessing rainfall variability at Adwa District, Central Tigray, Ethiopia." Int J Res Environmental Sci 5.1 (2019): 1-5.

    [GoogleScholar] [CrossRef]

26. Tujuba M, et al. "Rainfall seasonality and timing: implications for cereal crop production in Ethiopia." Agri Forest Meteor 310 (2021): 108633.

    [GoogleScholar] [CrossRef]

27. Stern, R. D., et al. "Analysing daily rainfall measurements to give agronomically useful results. I. Direct methods." Experimental Agriculture 18.3 (1982): 223-236.

    [GoogleScholar] [CrossRef]

28. R Stern, et al. "INSTAT climatic guide." Reading (UK): University of Reading (2006).

    [GoogleScholar] [CrossRef]

29. Zewdu T., et al. "Characterization and variability of Kiremt rainy season over Ethiopia." Meteo Atmospheric Phys 89.1 (2005): 153-180.

    [GoogleScholar] [CrossRef]

30. Mapunda, A., et al. "Livestock market data: Collection, dissemination and use in Tanzania." Livestock Data Innova Africa Brief (2011).

    [GoogleScholar] [CrossRef]

31. Seleshi, Zanke U. "Recent changes in rainfall and rainy days in Ethiopia." Int J Climatology: A J Royal Meteor Soc 24.8 (2004): 973-983.

    [GoogleScholar] [CrossRef]

32. Bewket, W. "Rainfall variability and crop production in Ethiopia: Case study in the Amhara region." Proceedings of the 16th international conference of Ethiopian studies. Vol. 3. Trondheim, Norway: Norwegian Univ Science Technology, 2009.

    [GoogleScholar] [CrossRef]

33. Conway, D. "The climate and hydrology of the Upper Blue Nile River." Geogr J 166.1 (2000): 49-62.

    [GoogleScholar] [CrossRef]

34. Williams, A., et al. "A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa." Climate Dynamics 37.11 (2011): 2417-2435.

    [GoogleScholar] [CrossRef]

35. Obsi D, et al. "The impacts of climate change on African continent and the way forward." J  Eco Natural environment 7.10 (2015): 256-262.

    [GoogleScholar] [CrossRef]

36. Obsi D, et al. "Meteorological data trend analysis and local community perception towards climate change: a case study of Jimma City, Southwestern Ethiopia." Envt, Develop  Sustainability 23.4 (2021): 5885-5903.

    [GoogleScholar] [CrossRef]

37. Babiso B , 2017. Spatial and Temporal Rainfall Trend Analysis: A Case Study of South Western, Ethiopia. Civil Environ Res.9(8) 2017.
38. Maren, R et al. "Indigenous knowledge for seasonal weather and climate forecasting across East Africa." Climatic Change 156.4 (2019): 509-526.

    [GoogleScholar] [CrossRef]

39. Guido, Zack, et al. "Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa." Climate Risk Management 30 (2020): 100247.

    [GoogleScholar] [CrossRef]

40. Hadgu,G et al. "Trend and variability of rainfall in Tigray, northern Ethiopia: analysis of meteorological data and farmers' perception." Academia J Research 1.6 (2013): 088-100.

    [GoogleScholar] [CrossRef]

41. Fanghua, H et al. "Temporal rainfall patterns with water partitioning impacts on maize yield in a freeze–thaw zone." J hydrology 486 (2013): 412-419.

    [GoogleScholar] [CrossRef]

42. Tujuba M, et al. "Rainfall seasonality and timing: implications for cereal crop production in Ethiopia." Agricultural Forest Meteo 310 (2021): 108633
43. Amogne A, et al. "Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin." Weather Climate Extremes 19 (2018): 29-41.

    [GoogleScholar] [CrossRef]

44. Dereje, A., et al. "Variability of rainfall and its current trend in Amhara region, Ethiopia." African J Agricultural Res 7.10 (2012): 1475-1486.

    [GoogleScholar] [CrossRef]

45. Walker S et al. "Matching of crop and environment for optimal water use: the case of Ethiopia." Physics Chem Earth, Parts A/B/C 29.15-18 (2004): 1061-1067.

    [GoogleScholar] [CrossRef]

46. Abbink, G. J. Ethiopian-Eritrean studies: a bibliography on society and history, 2010-2015. African Studies Centre Leiden (ASCL), 2016. [GoogleScholar] [CrossRef]
47. Hansen, J.,et al. Scaling up climate services for smallholder farmers: Learning from practice. Climate Risk Management, 22, 1–3. 

    [GoogleScholar] [CrossRef]

48. Woldemlak, B., and Conway D. "A note on temporal and spatial variability of rainfall in drought prone Amhara regions of Ethiopia." International J Climatology 27 (2009): 1467-147.

    [GoogleScholar] [CrossRef]

49. Rachel, J and Washington R. "Changes in African temperature and precipitation associated with degrees of global warming." Climatic change 117.4 (2013): 859-872.

    [GoogleScholar] [CrossRef]

50. Mirza, M.M.Q. (2003) Climate Change and Extreme Weather Events: Can Developing Countries Adapt? Climate Policy. 3 (3), pp. 233-248.

    [GoogleScholar] [CrossRef]

51. Kebede, W., et al. "Anthropogenic nitrate contamination of water resources in Ethiopia: an overview." Water Supply (2022).

    [GoogleScholar] [CrossRef]

52. Muchie Y, et al. "Rethink the interlink between land degradation and livelihood of rural communities in Chilga district, Northwest Ethiopia." J Ecology Envir 42.1 (2018): 1-11.

    [GoogleScholar]

53. Tujub M, et al. "Rainfall seasonality and timing: implications for cereal crop production in Ethiopia." Agricultural Forest Meteor 310 (2021): 108633.

    [GoogleScholar] [CrossRef]

54. Nayak, D. "Analyzing the perceived prioritized forest ecosystem services under the participatory management: A case of Maksegnit District, Amhara Regional State, Ethiopia." Trees, Forests and People 9 (2022): 100318.

    [GoogleScholar] [CrossRef]

55. Mpandeli,S. "Smallholder farmers’ awareness and perceptions of climate change in Adama district, central rift valley of Ethiopia." Weather and Climate Extremes 26 (2019): 100230.

    [GoogleScholar] [CrossRef

56. Walker S. "Matching of crop and environment for optimal water use: the case of Ethiopia." Phy Chemistry Earth,  29.15-18 (2004): 1061-1067.

    [GoogleScholar] [CrossRef]