The Impact of Rainfall Variability and Crop Production on Vertisols in the Central Highlands of Ethiopia

doi:10.21203/rs.3.rs-2004383/v1

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The Impact of Rainfall Variability and Crop Production on Vertisols in the Central Highlands of Ethiopia

https://doi.org/10.21203/rs.3.rs-2004383/v1

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Understanding the yearly, seasonal, monthly, and weekly rainfall variability is crucial for improved agricultural practice in a country like Ethiopia, where agriculture depends on rainfall. In particular, knowledge of rainfall onset, withdrawal, amount, distribution, and the length of the crop growing period would protect farmers from crop damage due to climatic anomalies. This study collected and described thirty-nine years of rainfall data using the Markov chain model. Based on the rainfall probability levels at different threshold levels, the length of the dry and wet spells and the length of the growing period were determined. The study shows dependable rainfall at a 75% probability level commences in June. The chance of receiving greater than 10 mm at a 50% probability level starts in week 10 (5 March–11 March), with much discontinuity up to week 21st (21 May–27 May). The dependable weekly rainfall begins the week of 22 May (28th May-3rd June) with a probability of greater than 20 mm. The study revealed that the short rainy season rainfall (February to May) is unreliable for growing crops at Ghinchi as opposed to other highland areas of Ethiopia. The length of the growing season is therefore confined to periods of the long rainy season (weeks 22nd to 39th, or 28th May-30th September). The water balance for the study area indicates that the moisture availability index is greater than 0.5,and potential evapotranspiration is lower than precipitation during these months.

variability

probability

Markov chain model

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vertisol

Ethiopia is endowed with substantial water resources (Melesse et al., 2013). However, the sector is entirely rainfall dependent, and smallholder farmers plow their land under climate change and rainfall variability using traditional technologies of low input and low-output agricultural practices (Baye, 2017; Menale et al., 2011; Segele & Lamb, 2005). Farmers’ weak socioeconomic status, limited access to agricultural technologies, and limited options for diversifying crop production constrained the country’s water resources (Yigezu Wendimu, 2021). The traditional rain-fed production system is subsistence-oriented, and smallholder farmers suffer from food shortages (Awulachew & Ayana, 2011) due to rainfall variability and recurrent droughts. In addition to rainfall variability, soil fertility depletion is prevalent on vertisols and nitisols of the Ethiopian highlands. In particular, vertisols are prone to land degradation and soil and water erosion, causing soil nutrient losses (Elias et al., 2022; Kharchenko, 2011; Dubale, 2001; Tekalign Mamo, 1988).

Vertisols are one of the major soil types covering a large portion of the Ethiopian highlands (Elias et al., 2022; Srivastava et al., 1989). The Ethiopian vertisols are moderately fertile, but rainfall variability, physical characteristics, and unsustainable land management practices limit crop production. The dominant clay minerals of vertisols prohibit land management practices in the wet and dry states. The soils have a narrow range between drought stress and excess moisture. Vertisols are soils that develop cracks when dry and sticky and plastic when wet (Elias, 2019; Tekalign et al., 1993). Shortage or excess rainfall causes management problems in land preparation, crop planting, and implementation of agronomic practices (Manik et al., 2019) Tekalign et al., 1993; Tekalign Mamo, 1988). Soil and water erosion and nutrient loss are prevalent due to unfavorable soil and water relationships. Waterlogging is a major problem for traditional farming practices because the soil is sticky when wet and hard when dry prohibiting local oxen from pulling the farm implements. The workability of the soils under traditional farming systems becomes difficult (Patil et al., 2016; Erkossa et al., 2004). Therefore, predicting rainfall variability, time of onset and withdrawal, amount, and distribution has to be studied for sustainable crop production on vertisols.

Ethiopia is susceptible to frequent draughts, usually related to the deficiency in total annual and seasonal rainfall amounts. The result is a food shortage, hunger for humans, and the death of livestock. From 1972 until the current year, the country experienced 17 years of drought (Mera, 2018).

Rainfall variability impacts soil water availability to crops, posing crop production hazards in Ethiopia’s highlands, where agriculture is a significant economic activity. Farmers usually rely on prior knowledge of weather conditions when planning farm activities. However, in a changing climate, knowledge of climate change and rainfall variability becomes important for traditional farmers to plan agricultural management practices. Excessive precipitation increases soil moisture and thereby enhances waterlogging, surface runoff, and erosion (Tian et al., 2021; Sehler et al., 2019; Pimentel et al., 2007), while less rainfall in a dry environment leads to reduced organic matter and nutrient availability (Jeong et al., 2018; Dairaku et al., 2004). Changes in precipitation affect vegetation, which has an impact on the soil organic matter cycle and changes in soil properties (Bansal et al., 2014).

Climate change, rainfall variability, and soil properties determine the sustainability of any agricultural production (Pulwarty et al., 2007). The proper ecosystem services and functions of soil determine the optimal relationship between soils and water, minerals, organic matter, and organisms. Among the most critical soil functions are temperature regulation, carbon and nutrient cycling, organic matter decomposition, and habitat for microorganisms (Sehler et al., 2019; Breure, 2004). All these factors determine the productivity of soils.

The Vertisol farmers of the study area grow crops on residual moisture just after the withdrawal of rainfall sometime in mid-September or early October. The practice is to escape the June–September rainy season due to fear of waterlogging. The soil is vulnerable to erosion when land is left fallow during the rainy season, and crops grown on residual moisture may suffer from thermal drought and dry spells, reducing crop yields. Agricultural planning on vertisols requires a deep understanding of the time of onset, withdrawal, amount of rainfall, and length of the crop growing period. Overcoming these challenges would minimize weather-related hazards and help farmers utilize the full potential of the crop growing season for sustainable crop production. Studying the distribution and amount of rain for a given area gives crucial information on agricultural planning and management practices (Journal et al., 2009; Abeyasekera et al., 1983; Virmani et al., 1982).

Therefore, for agricultural development and lowering climatic risk, it is crucial to understand and research the agro-climatic characteristics of a particular place. Knowledge of rainfall characteristics (onset, withdrawal, duration, seasonal totals, and dry and wet spells) provides a better understanding of rainfall variability (Manik et al., 2019), which is a crucial criterion for strategic agricultural development planning (Journal et al., 2009; Virmani et al., 1982). For this purpose, the long-term rainfall data of the Ghinchi area was interpreted using the Markov chain model (Appendix A). Therefore, the goals of this study were: (i) to examine the pattern of rainfall distribution, (ii) to estimate expected rainfall amounts at various probability levels, and (iii) to determine the start and end of the crop growing period using a Markov chain model of the first order, and (iv) to interpret climatic data for agricultural planning and operations as well as the timing of cultural practices involving Ghinchi Vertisol.

Description of the study area

Rainfall (R) data for 38 years (1982-2020) was provided by the First Class Weather Station at Ghinchi Agricultural Research Sub-Centre and the National Meteorological Agency at Addis Ababa, Ethiopia. The study area is 75 km west of Addis Ababa, in the Oromia region of Ethiopia, with geographical coordinates of 09⁰0’03” N, 38⁰0’30” E, with an elevation of 2200 m.a.s.l (Figure 1). The study area is flat to undulating topography dominated by dark black high clay soils (vertisols).

According to the traditional seasonal classification, based on rainfall amount and distribution, the Ghinci area has three seasons. The seasons are locally represented by kiremt—the long rainy season, belg—the short rainy season, and the bega—the dry season (Diro et al., 2011).

Data management and methods used

The Ghinchi agricultural research sub-center first-class meteorological station and the Ethiopian national meteorological agency provided the daily rainfall data from 1982 to 2020. The mean, standard deviation, and coefficient of variation of annual, seasonal, monthly, and weekly rainfall were computed using the information received. A five-year moving average analysis was used to estimate annual rainfall trends. Using historical rainfall data, the first-order Markov chain model, the initial and conditional probability, and the incomplete gamma model were utilized to assess the likelihood and expected amounts of rainfall (Liu et al., 2009; Mugalavai et al., 2008; Jail et al., 2005; Ochola & Kerkides, 2003). Delhi (1988) used the incomplete gamma distribution technique to compute the monthly rainfall amount at 10, 25, 50, 75, and 90% probability levels using long-term daily rainfall data gathered using standard meteorological weeks (SMW). The models can estimate the likelihood of future rainfall variability at a tolerable threshold level.

The Markov Chain probability model (Appendix A) was used for weekly rainfall of > 10, > 20, > 30, > 40, and > 50 mm based on the quantity of precipitation received each week. Studying the probability levels focused on the conditional probability levels of a rainy week after a wet week, a wet week following a dry week, and the beginning probability of rain this week being wet (Table 2; Appendix C). A threshold level of between 10 and 20 mm of rain in a week was chosen for agricultural management activities. At different probability levels, anticipated rainfall amounts were calculated using an imperfect gamma model (Robertson, 1976; Virmani et al., 1982).

The monthly probability at 10, 25, 50, 75, and 90% confidence levels were calculated using an incomplete Gamma distribution. Reliable rainfall is defined as the quantity of precipitation received with a probability of 75% for a Gamma distribution by Hargreaves, H.G. (1975). The 50 and 75% likelihood levels serve as the threshold values to indicate rainfall during the growing season. The 75% likelihood level is normally used to establish the start and end of the growing season rainfall. The combined loss of water by transpiring plants and evaporating water from the earth, influenced by temperature, humidity, sunlight, and the wind, is known as the potential evapotranspiration (PET).

The end of the growing season was determined as rainfall below 10 mm at a 50 % probability level (Gramow and Henery, 1972). A value of R/PET >0.75 (R-rainfall) was also chosen because it represents an optimum range of plant growth. The start of sowing rains is considered to be the week that comes just before the start of the available effective rainy period. Generally, this is the amount of rain needed to wet the topsoil layer to allow seed germination. It is specified by R/PET >0.50, in addition to a continuous humid period later. The length of the growing season was then estimated using the calculated start and end dates. Since the other climatic factors are often tropical, steady, and predictable, this paper skips a full analysis of these weather factors.

As Zotarelli et al. (2014) described, Penman’s model was used to estimate the weekly and monthly PET values. The ratio of assured rainfall at 50 and 75% probability levels to PET on a monthly and weekly basis is known as the moisture availability index (MAI), and an MAI of greater than 0.5 was deemed adequate for growing crops (Hargreaves, H.G., 1975). Based on the determined start and withdrawal of rainfall dates, the length of the growing season was then estimated. If there is a greater than 50% chance of obtaining 20 mm of precipitation per week and the following week is rainy or wet, the growing season begins (Reddy, 1983). Therefore, planting would be possible in the first week following February if there was a 50% chance of receiving at least 20 mm of rain in back-to-back days without a ten-day dry period (Journal et al., 2009; Abeyasekera et al., 1983).

Annual rainfall statistics

The recent trend in climate change and rainfall variability affects crop production in developing nations like Ethiopia, where rain-fed agriculture dominates the farming system (Alemayehu et al., 2020; Dereje Ayalew, 2012). Ethiopia has a wide range of agroecologies, from higher (>4550 m.a.s.l.) to 125 m below sea level. Rainfall and temperatures considerably vary depending on altitude and topographic features (World Bank 2006).

Figure 2 shows the study area’s annual, long-term mean, and five-year average rainfall. The long-term average annual rainfall was 1099 mm. The yearly rainfall varied from 813 mm in 2002 to 1456 mm in 1988, having high variability between years. In 23 instances (59%), the long-term mean rainfall is less than the individual years, while in 16 instances (41%), the long-term mean rainfall is higher. Reduced crop yields may result from less than the long-term average rainfall. The coefficient of variation and annual standard deviation were, respectively, and. These variables display the rainfall amount’s departure from the mean.

The rainfall quantities of the five-year moving average were six times (75%) lower than the long-term norm. Only twice did the five-year moving average amount exceed the rainfall average over a long period. In particular, since 2000, the continuous decline in yearly rainfall below the long-term mean indicates the need for the selection of crops with less water requirement. The annual rainfall average may indicate how well a region’s agriculture is doing. On the other hand, the likelihood of yearly rainfall provides crucial initial information for creating alternative farming strategies. It is not easy to make decisions regarding agricultural planning because annual rainfall typically only provides a general picture for a particular area (Zakwan & Ara, 2019). Six of the eight years with moving average rainfall had rainfall below the long-term mean. The five-year moving average rainfall is only twice as high as the average annual rainfall over that 39 years.

Seasonal rainfall variability

The study area, Ghinchi, is characterized by a bio-modal rainfall pattern of two rainy seasons, the long and short. About 699.9 mm (64%) of the rainfall is received during the long-rainy period, 308.9 mm (28%) during the short rainy season, and the rest 90.8 (8%) during the dry period (Figure 3). The long-term annual rainfall shows a decrease in amount since 2000. In effect, for the 39 years (1982-2020), the total average rainfall amount is declining yearly by an average of 351 and 267.2 for long and short rainy seasons, respectively.

The rainfall pattern, amount, and distribution greatly vary between the three seasons. Farmers in the Ghinchi area expect rainfall during the long rainy season starting in June. Sometimes, rainfall comes early or late, making traditional farming difficult. Vertisol sowing always needs precautions. As soon as the rain starts and the soil gets friable, planting is normally done on vertisols. Once the soil is saturated, the land becomes sticky and plastic; when dry, vertisol forms hard clods making farm operation difficult (Society, 2016). This is when the rainfall from the onset becomes intense with heavy showers. Sometimes, in years of above-average rainfall in the short rainy season, the long rainy season tends to be lower and vice-versa. Getting the optimum soil moisture range becomes difficult under changing climate and rainfall variability.

The Short rainy season rainfall is variable and not dependable in the area. The major limitation of the short rainy season regarding rainfall availability is its wide variability between years and seasons, often inadequate water supply. Rainfall may be seasonally adequate but of limited duration, variable in an amount from year to year and season within years, or chronically deficient. Therefore, short-duration crops such as forage legumes can be grown or supplemented with irrigation to grow long-duration crops. High and erratic rain during the long rainy season brings waterlogging to the farmlands. To overcome the waterlogging problem, farmers have traditional ways of draining the excess water from their farmlands. These practices are ridge and furrow, opening drainage furrows along the slopes at different spacing, and sowing crops on residual moisture when the long rainy season sometimes resides after September. Following the land during the long rainy season exposes the farmland to severe soil and water erosion, land degradation, and soil fertility depletion. Planting crops on residual moisture may also result in crop failure due to dry spells sometime in December-November.

Traditional farming practices cannot fully drain the water from the farmlands. Therefore, the research has developed the Broad bed and furrow maker (BBM), an implement that constructs the broad bed and furrow (BBF) capable of draining the excess water from the farmland. The BBM is a curved metal sheet attached to two unmodified traditional plows, usually oxen-drawn. Metal wings scoop the soil towards the center between the two plows (Astatke et al., 2001). The BBM builds raised beds 80 cm wide and alternates them with furrows 40 cm wide between the beds and 15 cm deep. The implement needs friable soil to make the necessary bed and drainage furrows. Crop production losses are inevitable if an intense and heavy shower comes at the onset of rain because of the soil puddles, and it becomes difficult for local oxen to pull the soil and make the bed. Continuous rainfall for a continuous period prohibits the use of farm implements on vertisols. That is why most farmers leave their farmland fallow and go on to plant pulse crops on residual moisture after September.

Monthly rainfall statistics and seasonal variability

For the studied area, monthly rainfall variability is relatively considerable. The long-term mean monthly rainfall ranged from the lowest of 9.9 mm in December to the highest of 242.1 mm in August. The long rainy season months have high rainfall, with the highest amounts received in July and August. The monthly rainfall’s standard deviation ranged from 16.3 to 67.4. The CV was between 21.5 and 206.7%. Compared to the other two seasons, the long rainy season’s rainfall has a lower coefficient of variance. The monthly average standard deviation and CV were 78.3 and 85.3%, respectively (Table 1). The lowest standard deviation was observed for the dry season, while almost similar amounts were observed for the long and short rainy seasons. The highest coefficient of variation was observed for the short rainy and dry periods, while the long rainy seasons had a lower variation coefficient. The highest and lowest monthly mean rainfall was 9.9 mm (December) to 242.1 mm (August), with the lowest value during the dry seasons.

Monthly rainfall and PET relationships

The study area’s long-term rainfall and PET data (2000–2020) were summed up to find the mean annual rainfall and PET, as illustrated in Figure 4. The data shows that almost eight months a year had PET higher than the rainfall. These months fall under the short rainy season and the dry periods (October–May). Higher PET values create a negative water balance, resulting in soil dryness and necessitating irrigation for crop production (Cui & Zornberg, 2008). During the short rainy season, in some parts of the highlands, the rainfall is inadequate to grow short and long-duration crops. However, particularly in the Ghinchi area, the short rainy season rainfall is not sufficient to plant crops. The short rainy season rain is important for farmers to prepare their land before the long rainy season rain commences.

The long rainy season (July–September) has rainfall above PET; therefore, the water requirement of crops can be met. Excess moisture may be expected during this period, causing waterlogging, flooding, and soil and water erosion hazards.

Weekly rainfall and PET relationships

The mean weekly rainfall was 82.3 mm. Thirty-one weeks (60%) had rainfall below the weekly average of 82.3, while remaining twenty-one weeks (41%) had rainfall above it. The maximum number of mean weekly rainfall less than 20 mm per was observed for weeks 2, 3, 4, 8, and weeks from 43^rd to 52^nd (Figure 5). These weeks are confined to the dry seasons. The rainfall during the dry season may cause problems with crops ready to be harvested or threshing because these activities are done during the dry season immediately after the withdrawal of the long rains.

On the other hand, the October rainfall may have negative and positive effects. Crops that grow on residual moisture favor the dry season rain, while crops planted during the long rainy season are ready to be harvested or threshing and cause discomfort. This shows the complexity of the farming system on Vertisols. During the long rainy season, rainfall above 100 mm is expected, the highest being 231.7. The weekly standard deviation was 69, and the average CV of the rainy season was above 100% for the rainy season. Lower CV was recorded for weekly rainfall during the short rainy and dry seasons (Figure 5). The highest CV for the long rainy season is explained by its high variability of low and high rainfall.

A trend in weekly rainfall distribution and PET was observed to that of monthly rainfall distribution and PET. Weekly rainfall was higher than the PET during the weeks from 22^nd (28 May-03 June) to 39^th (24 September-30 September (Figure 6). The maximum weekly rainfall was recorded during the 22^nd to 38^th (28 May to 23 September) weeks, with an average of 163.8 mm (68%). The weeks belong to the long rainy season and crop planting period. Overall, the mean weekly rainfall for the dry period was 16.8 mm (6%), while for the short rainy season, it was 61.5 mm (24%). The mean weekly rainfall distribution during the three seasons shows that the dry and short rainy period’s rainfall was below PET, while the long rainy season had rainfall higher than the PET.

Monthly and weekly rainfall amounts at a given probability level

Table 2 shows the monthly minimum rainfall quantities expected at five likelihood levels: 10, 25, 50, 75, and 90%. The data shows that at a 75% probability level, rainfall of more than 20 mm during the short rains can be expected from March to May but never exceeds 38 mm. During the short rainy season, the rainfall is far from the 50% value, which explains the high variability influenced by a few high values. The short rainy season rainfall is not sufficient to grow crops but adequate for pre-sowing land preparation before the onset of the long rainy season. In June, a sharp increase in rainfall was recorded (116 mm), an increase of 88 mm more than in May (42.3%). The probability of monthly gamma distribution at a 75% probability level ranged between 107 and 201 mm, the highest being in July (201 mm). The mean difference and 50% values in the long rainy season are similar, and the data represents a normal distribution. The abrupt increase during this period causes waterlogging on the soil, making planting difficult. Therefore, the soil requires drainage improvement. The chance of getting a certain rainfall amount is negligible in the drier season.

A comparison of the long-term mean monthly rainfall and the monthly expected rainfall amount at five probability levels shows different values (Tables 1 and 2). Rainfall at a 75% probability is more reliable (Hargreaves H.G. 1975) for agricultural crop management planning than the monthly mean rainfall amount. Compared to the annual average rainfall, dependable rainfall has lower values and is less skewed.

Weekly rainfall and initial and conditional probabilities

The Markov Chain probability model has been extensively used in agriculture to determine dry and wet spells, the onset and cessation of rainfall events, and to develop agricultural management operations. The Markov chain model was used to compute the long-term (39) year rainfall data using the initial probability level of receiving a certain amount of rainfall during a given week, i.e. [P(W)], the conditional probability level that predicts the likelihood of rain next week if we had rain this week [P(W/W)], and the likelihood of rain next week if this week is dry [P(W/D)]. The computed weekly rainfall probability shows the rainfall received for each threshold level of 10, 20, 30, 40, and 50 mm. The Weekly precipitation for the probabilities was analyzed by receiving a certain amount of rainfall threshold values such as 10, 20, 30, 40, and 50 mm. The probabilities of receiving a specific amount of precipitation in a week (Table 3) can be used as a threshold level for various crops, cultivars, or soil types with various water-holding capacities. This information helps determine what crops should be grown and what agricultural practices can be used. As expected, the longer the growing season and earlier the rainy season begin with decreasing threshold limits. It may sometimes be misleading due to a false start of rain. Therefore, dependable rainfall at a 75% probability level on weekly basses is more important (Hargreaves 1975; Stern et al., 1982), and careful consideration of the target environment and crop selection should be prioritized. For most field crops, at least > 20 mm of rainfall at a 75% probability level in a week could serve as a reasonable threshold. In this study, consecutive weeks with assured rainfall exceeding 20 mm were between the 22^nd and 38^th weeks. Hence, planting crops can be done during this period without the risk of moisture deficit. The optimum period for planting crops for Ghinchi is around mid to late June. The temperature, humidity, and rainfall for June and July are conducive to rapid growth. Nevertheless, the period covering late July through August experiences a high intensity of rainfall, more cloud cover, and more saturated soil, which aggravates the problem of waterlogging. When rainfall is in excess, improved drainage systems and water harvesting structures are recommended to supplement crops grown on residual moisture if a dry spell occurs. Using the rainfall totals for each week probability of rainfall has been computed by fitting a mathematical function (constant precipitation analysis).

The W/W probability indicates the continuity in rainfall and suggests those weeks are favorable for crop production. At a 50% probability level, the chances of receiving more than 10 mm in a week begin around week 10, although they are inconsistent until week 21, while increasing the threshold limits the probabilities close to the true value. Effective rainfall of more than 20 mm/week, at a 50% probability level, starts on the 22^nd week and extends to week 38 (16-22 September). At the 50-mm/week-threshold limit, they are confined to weeks 29-32 (15-21 July to 5-11 August) only, indicating that in these weeks, there are high chances of heavy rains and risk of soil erosion and waterlogging. In dry periods between October and January (weeks 40-52 and 1-4), the probability of getting at least 10 mm/week decreases, and the variability is very high. Particularly, weeks 45 and 46 had some rain at the 10-mm threshold level, which may interpret the harvest and threshing of crops. Considering 20 mm and above of rain in a week without discontinuity at a 50% probability level is sufficient for tillage and planting crops (Reddy, 1983; Virmani et al., 1982The period corresponds to weeks 22^nd to 37^th (June-mid September), which indicates the length of the growing period.

Dependable rainfall

Rainfall is the most important climatic component since it has a detrimental effect on crop productivity. Cropping patterns in different ecological zones vary depending on rainfall quantity, occurrence, variance, and reliability. In rainfall-dependent developing countries, climate change and variability cause crop failure and food insecurity. Therefore, rainfall probability study for a specific location becomes essential because agricultural production is highly affected by climate change and rainfall variability (Gitz et al., 2016; Chijioke et al., 2011).

Since there is always some variability in space and time, it is important to have a probability estimate using various models. Crop production can be sustained only under good soil conditions and conducive climatic conditions. Proper soil management, harnessing climatic risks, and improved agricultural technologies such as crop selection and irrigation practices have become prerequisites for planning agricultural activities and increasing crop productivity. Farmers will be better protected if climate trends are predicted because it will be possible to predict the effects of rainfall anomalies on crop production.

Monthly dependable rainfall and MAI

Monthly rainfall has more variability than dependable rainfall and estimates less rainfall for crops, while dependable rainfall gives reasonable estimates in line with global climatic changes. Dependable rainfall with a 75-80% probability of occurrence suffices for crop production (Hargreaves, G. H. 1975). Estimating dependable rainfall through long-term study and analysis of long-term climatic data helps to plan the water management for a specific area and crop type. If the probability of occurrence is above or below the threshold levels, agricultural crop management decisions will be made, such as irrigation or draining the excess water. For excessive rainfall, drainage improvement can overcome the problem, while under dry periods, the water requirement of crops will never be met.

The 50 and 75% probability levels are the periods of weeks in which the chances of getting a dependable rainfall of more than 20 mm/week are adequate. The months coincide with the farmer’s planting date for the long rainy season. The length of the growing season depends on the onset of the rain; therefore, in this study, the length of the growing season varied between 70 and 133 days for the short and long rainy seasons, respectively. Weeks 25^th to 32^nd had assured rainfall of 100%, with a high risk of erosion and waterlogging. The rainfall parameters such as onset, withdrawal, amount, distribution cessation, and the probability of receiving dependable rainfall in a month or week will be made on the management options and type of crops to be planted.

For the Ghinchi area, the amount and distribution of annual rainfall and the growing season’s length are closely related. The dependable rainfall and PET relationship (Figure 7) also shows that PET drops below the mean annual rainfall during the long rainy season, while vice-versa during the short and dry seasons of the year.

The 75% probability level indicates that dependable monthly rainfall begins in June, while it begins around May at a 50% probability level. The monthly PET reveals a positive water balance from June to September. Rainfall throughout the remaining months is below PET; thus, short-duration crops can probably be cultivated with the help of additional irrigation. Considering the 50% probability level, precaution should be made that the lower the probability level, the higher the threshold values, but the dependability matters. The relationship between rainfall, PET, and MAI is shown in Figure 8. According to these data, at a 75% probability level, rainfall exceeds PET from July to September, while at a 50% probability level, it covers between May and September. The weekly and monthly rainfall probability levels indicate that an MAI of more than 0.75 starts in June. Sowing of wet season crops occurs in June, while pulse crops grow on residual soil moisture after September. The relationship between rainfall and PET shows that PET is lower than rainfall starting from week 22nd to week 38th. The positive water balance during this period allows long-duration crops to grow because the weekly rainfall is dependable. The MAI is at a 75% probability level, at which > 20 mm of rain is probably in this range.

Weekly dependable rainfall and MAI

Understanding the relationship between rainfall and PET, and MAI which is computed as the ratio of 75% dependable rainfall to PET, is important for agricultural planning and irrigation requirements. Figure 9 illustrates weekly average and dependable rainfall at a 75% probability level, including MAI and PE for Shinichi. Rainfall above PET (R/PET) is expected during this month. Sometimes, due to the intermittent and patchy character of the rain, it is unpredictable to establish the exact sowing week and cessation of the rainfall. Rainfall above PET (R/PET) is the period that rain falls during the four months that are the main crop-growing periods. Sometimes, due to the intermittent and patchy character of the rain, it is unpredictable from year to year to determine the beginning and cessation of the rain weekly. PET is lower than rainfall, and crop growth requirements can be maintained during these months. An MAI value of 0.33 is considered the threshold value to demarcate dry and moderate wet periods (Hargreaves et al., 1985). This value is significant at a 75% probability level and even higher at an MAI value up to the 38th week. In the rainy months, rainfall greater than 75 of PET results in waterlogging and runoff.

The MAI of Ghinchi is more than 1.33 from the 21st to the 38th week, indicating there is no shortage of water for agricultural activities during this period. However, the rest of the months are always short of moisture.

The effective rainfall

An effective measure of the water balance is the computed ratio of 75% dependable rainfall and PET, defined as MAI. At Ghinchi, the long rainfall season commences in June and extends up to September. During the months, about 64% of the annual is received. Water requirement calculation at a 50% probability can be used to check the ranking of the Gamma distribution (Hargreaves et al., 1985). Table 2 compares the mean (the 50% probability) and the dependable (75%) probability of assured rainfall for the long rainy season rainfall at Ghinchi using long-term data. The 75% probable rainfall values are less than the mean rainfall and can be used for computations. The ratio of assured rainfall to PET, calculated weekly, gives a better understanding of water availability to plants, as a month is a long period (Hargreaves H.G. 1975). Weekly rainfall at a 75% probability level and an MAI greater than 0.5 was considered optimal for plant growth in this study area. The weekly rainfall and PET data, averaged over 39 years, are shown in Figure 6. The dependable rainfall at a 50 and a 75% probability level exceeds PET, starting in weeks 23^rd and 25^th and ending in weeks 38 and 36, respectively, showing a positive water balance. The remaining weeks had a negative water balance, meaning PET is greater than the dependable weekly rainfall. Thus, on average, for 16 weeks a year, the climatic water balance is positive, while for 35 weeks, the climatic water balance is negative. The mean annual rainfall of the study area is 1099 mm, while the mean annual PET is 1412 mm, indicating that the annual water deficit is 313 mm. According to Hargreaves (1975), the value of MA1 > 0.34 could be considered the lower value for dryland crops. The MA1 values exceed the lower threshold value of 0.34 in all the rainy months of Ghinchi, and the data for the length of the rainy seasons show that there are 133 days in Ghinchi (19^th to 38^th weeks), which is 280 days. During the 1^st to 17^th weeks and the 40^th to 52^nd weeks, MA1 values are below the lower threshold value. The MAI exceeds 0.5 falls from June to September (22nd–40^th weeks), which indicates a crop growing period. However, MA1 during the 40^th and 41^stweeks was low. In some years, the MAI in October becomes short of the total water requirement for crops grown on residual moisture. MA1 at a 50% probability level falls between the 25^th and 35^th weeks, which is more than 1.00, indicates excess water, and requires an improved drainage system. The high water availability in this period would be sufficient to meet the moisture requirement of the crops in the latter part of the season. In case of surplus rainfall, a water harvesting structure must be constructed to supplement crops suffering from dry spells sometime after September.

Every month, the ratio of R/PET was below 1.00 from January to May and then dropped below the same threshold value of 1.00 from October to December. June and September had adequate rainfall, while July and August had >1.33 signals that the soil is saturated due to excessive rainfall, and draining the excess water from the farmland is crucial (FAO, 2009; Hargreaves, 1971).

The rainy days

Continued rainfall data is crucial to assess the probability of dry and wet spells. It can be made possible by examining and summarizing some of the long-term climatic records of a study area. This information for calculating the probability of any specified combination of “wet” and “dry” days may be required to make long-term management decisions. The study confirmed that the amount of rain per rainy day and the number of rainy days increased from June to September and then declined. As evidenced in Table 4, the wet day count in July and August is more than 250 days and starts to decrease from September onwards. Wet day count above 100 started in March, reached 265 days, and then declined. The dry day count of more than 100 days started in September, reached 300 days in December, and then declined steadily. The lowest dry day counts below 100 days were for June, July, and August.

Dry counts were lower between February and May. The wet day count following a dry day count was higher for the short rainy season, while the values were lower for the main rainy season. The wet/wet day count was higher starting in May and extending up to September. The mean minimum rainfall amount was 0.25 mm for the dry periods. The mean maximum rainfall was 38.9 mm. The highest maximum rainfall recorded was in August, which was 75.9. The other weather parameters shown in Table 4 are without clear differences between months.

Length of the growing period

At Ghinchi, the number of rainy days and the amount of rain per rainy day increased from June to August and then declined. June and August are the most assuredly rainy months, as is evident from the average duration between the rainy days (Table 4). The probability of receiving a dependable rainfall of 20 mm is 50% in the 22nd week and extends up to the 38th week. The week coincides with the farmers’ planting time during the long rainy season. For the short rainy season, the probability of receiving at least 10 mm of rainfall at a 50% probability starts around week 16, with some discontinuity in the preceding weeks. However, the chance of getting rainfall above 20 mm/week at a 50% probability for a short rainy season coincides with the long rainy season. As the length of the season mainly depends on the starting date, the effective length of the rainy season was found to vary between 120 days at a 90% probability level and 210 days at a 75% probability level (Table 5). Therefore, depending on the onset and withdrawal of rainfall, a decision will be made on the type of crop to be grown. However, to consider the length of the growing period, the dependable rainfall at a 75% probability level and a threshold level of greater than 20 mm with a conditional probability of [P(W/W) is crucial. In this connection, the length of the growing season is about 17 weeks (119 days). The different threshold limits give different values for the growing season, which depends on the type of crops to be grown, the water requirement of crops, and soil suitability.

Other climatic elements

There is no major difference in weather parameters recorded for the last ten years (Table 4). The maximum and minimum temperature ranges have relatively few variations, with a small standard deviation. The average yearly relative humidity varied between 50 and 84. Approximately 12 and 38 MJ/m2/day of solar radiation per month were measured. This study did not include weather parameters because they are consistent and predictable in all years.

Crop production on vertisols

A large variety of crops are grown in the area under various agricultural systems, allowing for much flexibility. The traditional farming practice depends on the onset of rain to grow crops. However, growing crops do not consider the water requirements, the potential yield, and the soil capability growing crops. To assess the potential use of seasonal forecasts, the type of decisions and the factors, including the climate, that affect them need to be documented. An assessment was made to characterize some of the decisions made by farmers to improve their livelihood. The various farming practices on vertisols specific to this area are planting long-duration crops such as Maize and sorghum if the rains come during March/ April (short rainy season). The crops will grow using short and long rains. Otherwise, the lands are left fallow for grazing livestock. However, the land allocation decisions for each crop depend on a range of factors, which vary each year, including other resources such as labor and land preparation. Most crops are planted during the long rainy season, with minimal input and no irrigation facilities if weather anomalies occur. If the rain delays during the long rainy season, teff can be planted in late June.

Teff requires much labor throughout the planting, harvesting, and threshing processes. Teff also prefers waterlogged soil conditions and needs a fine seedbed preparation. The agro-climatic data analysis may indicate possible options for diversifying crops on these soils. The performance of different combinations of cropping systems under different rainfall distributions can be tested. Together with the seasonal rainfall forecasts, the probabilities can help to develop the various scenarios available to the farmers, whatever level of risk they choose. Some decisions that can be made affect the land preparation, crop and variety choice, fertilizer application rate, soil/water conservation measures, and disease and pest control practices. The potential of double cropping or crop choice according to the seasonal rainfall forecast and the development of water harvesting structures can maximize crop production in the area.

Potential yield and Crop Production Constraints

Vertisols are the soil with high potential for crop production, but their physical characteristics limit their use and full potential. In many highland vertisol areas, farmers overcome the waterlogging problem by planting on residual moisture toward the end of the rainy season. Using only part of the growing season leads to low crop yields and considerable erosion because fields are without vegetative cover for much of the time. Today, most farmers use the broad bed and furrow drainage system to overcome the waterlogging problem and plant crops early in the season. However, the yield of crops grown on these soils is still much lower compared to other developing countries. To analyze the rainfall-soil-yield relationships, 20 years of rainfall and wheat yield data were taken, and regression analyses were performed (data not shown).

However, it turned out that the relationship between crop production and total annual or growing season rainfall was so weak that it could not adequately convey the effects of rainfall variability on crop productivity. Rainfall throughout the growing season, particularly rainfall in June, July, August, and September, has a little link with crop productivity. The linear model approach’s key flaws were that it could not distinguish between negative and positive effects while also recognizing a wider range of years with normal or around normal yield levels. However, in certain years, negative yield anomalies signified rainfall’s negative impacts, just as in other years, positive anomalies signified positive impacts. On Vertisols, negative impacts of climate are more likely to result from excess rainfall and poor drainage system during the crop growing period. For wheat, positive climate impacts were recorded in 1993, 1995, and 1997. In the other year’s crop yield anomalies did not attain the levels at which they could be considered impacts. This is not sufficient for forecasting crop yields or the impacts of climate. As most Ethiopian agriculture is rain-fed and crop-livestock integration is very high, yield reduction due to rainfall variability affects the whole farming system.

The rain-fed subsistence agriculture in Ethiopia is a victim of climate change and rainfall variability. Under the variable climatic regime, the traditional farming system cannot feed its population, and recurrent drought is becoming a common phenomenon. Several researchers have tried to predict and forecast climate change and rainfall variability for different parts of the country. However, none of these results reached the target community, the farmers. At least it is very important to recommend the onset, cessation, and length of the growing season for a specific area. We have tried to identify the onset and withdrawal of rainfall in the Ghinchi area from the long-term rainfall data analysis. The appropriate planting time for the Ghinchi area could be the last week of May to Mid-June. At Ghinci, like in other highland areas, Short season cropping is unlikely because the rainfall is unreliable and PET is higher than rainfall. Maybe long-duration crops can be planted in April-May to capture the long-season rainfall immediately. The water holding capacity of vertisols is very high. Therefore, double cropping can be exercised in good years of above-average rainfall. Planting wheat early in the season using an improved drainage system (early June) followed by pulse planting crops in early October can be tested. Constructing a water harvesting structure is also a promising venture for Vertisols to overcome dry spells. Our findings show that the chances of receiving more than 50% rainfall at > 20 mm (P(W/W)) threshold level begins on the 20th (14–20 May) and ends on the 36th (3–9 September). Based on this calculation, the length of the growing period becomes four months. Dependable rainfall at a 75% probability level starts in June and extends until September, with 116, 201, 187, and 107 mm for June, July, August, and September, respectively. Forecasting the likely onset and cessation of precipitation on vertisols help farmers prepare their land, plant crops, and harvest on time before the soil becomes saturated or dries out, making farm operation difficult under the traditional farming system. During years of excess rain, improving the drainage system and constructing water harvesting structures becomes useful so that farmers supplement their land if an early withdrawal of rainfall occurs at any time of the year.

Acknowledgements

The authors greatly appreciate the Holetta Agricultural research center and Ghinchi research sub-center for providing the required meteorological data.

Authors’ contributions

As part of a PhD study for the corresponding author, the co-authors are advisors and supervisors of this study. Therefore, their contribution to this manuscript was profound. Their contribution was higher in reviewing, editing and interpretation of results. All authors read and approved the final manuscript.

Funding

This study got a small amount of money for data collection and transportation from Addis Ababa University.

Availability of data and materials

The long term rainfall data of Ghinchi research sub-center was provided from Holetta agricultural research center. .

Ethics approval and consent to participate

This manuscript is part of a Ph.D. Thesis- “The Impact of Climate Change and Rainfall Variability in Relation to Drainage Improvement on Vertisols for Sustainable Crop Production in the highlands of Ethiopia". Therefore, all authors agreed and approved to be published and there is no ethical conflict

Consent for publication

All authors read the manuscript and agreed to publication.

Competing interests

The authors declare that they have no competing financial or non-financial interests directly related to the work submitted for publication.

Author details

¹ Center for Environmental Science, College of Natural and Computational Sciences, Addis Ababa University, P. O. Box 1176, Addis Ababa, Ethiopia. ² Institute of Geophysics Space Science and Astronomy, Addis Ababa University, PO Box 1176, Addis Ababa, Ethiopia. ³ Center for Food Security Studies, College of Development Studies, Addis Ababa University Ethiopia, Addis Ababa University, PO Box 150129, Addis Ababa, Ethiopia. ⁴ Center for Food Security Studies, College of Development Studies, Addis Ababa University Ethiopia, Addis Ababa University, PO Box 150129, Addis Ababa, Ethiopia. ⁵ Department of physics, Collage of Natural Resource and Computational Sciences, Wolkite University, Wolkite, Ethiopia

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Table 1 To 5 is available in the Supplementary Files section.

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The Impact of Rainfall Variability and Crop Production on Vertisols in the Central Highlands of Ethiopia

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