Ethiopia is a county in Northeast Africa (figure 1) with over more than 90 million people. The major sector of the country is the agriculture sector, and around 85% of the population works within this sector (Fantaye, n.d.). The small farmers produce 94 percent of the food crops (BEKELE), and the other 6% is dominated by the private and state commercial farms. Although the agriculture sector is the most dominated sector of Ethiopia, the productivity is pretty low and cannot measure up with the food need of the country. In 2015, 32% of the total population was undernourished. (The FAO hunger maps 2015, 2015). Even right now Ethiopia struggles with food security due to a pretty strong warming up of the ocean water called El Niño (El Niño strikes Ethiopia, 2016). The croplands of the small farmers depend on the rainfall, and that is why with long periods of drought the crop production will fail and the people will famish.
Figure 1 Location of Ethiopia. (About Ethiopia, 2012). Ethiopia lays in the Horn of Africa.
In Ethiopia there are different agro-ecological zones. Agro-ecological zones are areas with the same climate, soil and land surface characteristics. In Ethiopia these zones are mostly categorized due the different height within the country (appendix 1.1). There are the dry lowlands closed to Somalia with a minimum rainfall of 400 mm a year and you have got the central highland with an annual rainfall between 950-1500 mm a year (Fantaye, n.d.). In the lowland you have got mostly pastoral areas, while in the highland different croplands are dominated. Ethiopia does not only have differences in heights but also knows different rain seasons. There are mostly called the Belg and Meher season. (Rashid P. D., 2012) For the Belg season most of the rain falls in the months March and May, and is mostly known as the shorter rain season. In the Meher season, most of the rain falls between June and September, this is also why this season is mostly known as the longer rain season. The Meher rain is the dominated rain season in Ethiopia and so also the main growing period of crops of Ethiopia. The cereal production in the Meher season is even about 90% of the total cereal production (USDA-FAS, 2008). But in some areas it hardly rains in the Meher season, and is the Belg season more present. Over here another growing period should be used then the most commend one, and only crops with a short growing cycle can be successfully applied.
The type of crops that grow in Ethiopia are mainly cereals, pulses and oilseeds, but there are also land with cash crops like coffee, sugarcane, cotton, fruits and vegetables. The production of cereal is the most dominated crop in Ethiopia. About 85% production by area is the production of cereals (Mengistu, 2003) The cereals crops grow in most of the regions, but essentially in the mid- and highlands. The different types of cereal crops are maize, sorghum, barley, wheat, millet and teff which is an indigenous staple source of food.
Due to the fact that these major food crops have the highest yield and are the most important for the food security of Ethiopia, the target of this report will be mostly about the four major cereal crops, which are maize, sorghum, barley and wheat. Besides, it will specially look at the small farmers which are also with 94%, the main group of farmers. The aim is to design financial or/and hydrological instruments for specific areas in Ethiopia, so with small adjustments the marginal farmers will have a more efficient production of their crops, the resilience against extreme droughts will be higher and by this the food deficit will reduce as well.
The method and data
There will be 3 types of analyses where the final recommendation will make mainly use of. The first one is with the help of QGIS, and will be about the monthly precipitation and the crops specific evaporation. The second analysis will take a more detail look at specific locations and at the daily rainfall. Over here a dry spell analysis will be made. At last, the commodity prices of the four crops will be studied, to find out if there are big differences within these values.
QGIS
To analyse the data the program QGIS, a Quantum Geographic Information System, is used. With this program different data sets can be viewed, edited and analysed. The program make use of vector and raster layers. A vector layer consists of geometric objects and will not lose its quality while zooming ing. While a raster layer different grids and does lose its quality while zooming in. This quality depends on the resolution of the raster layer. For all the data the same vector layer of Ethiopia has been used. This vector layer comes from the website of the Global Administrative Areas (Hijmans, 2015), and for all the data, that had been analysed by QGIS, the vector layer of Ethiopia with 79 zones (figure 2) has been applied. On the other hand, sometimes the names of the main regions are mentioned (Appendix 3.1). The resolution of the obtained raster data difference between the data sets. Some data have a higher resolution than other data sets. The three datasets that have been analysed with QGIS are the monthly precipitation, monthly PET and land cover.
Figure 2 Vector layer of Ethiopia used for QGIS. This vector layer divides Ethiopia into 79 zones. It has been used to calculate per zone the monthly precipitation, monthly PET and the land cover.
Monthly Precipitation:
The data for the analysis of the monthly precipitation has been obtained by the website from the World Climate Data (Data for current conditions, 2016). The data that has been used, has a 30 arc-seconds grid resolution, which means that the spacing between the points is about approximately 1 kilometre. The data is obtained over 30 years, between 1960-1990, for some point this period was even longer, and for other this period was shorter. The unit of the data is in mm.
Potential Evapo-Transpiration (PET):
Potential evapotranspiration is the maximum amount of water that the earth atmosphere is able to remove, if the amount of water would not be limited. The water will be removed through the process of evaporation, which is the process when liquid water is transformed into gas, and transpiration, which is the process when water is removed through the opening areas into plants. The data for the PET has been taken from CGIAR International Research Centres (Global-PET and Global-aridity, n.d.). The standard method that CGIAR used to calculate the PET was the Penman-Monteith equation. Just like the monthly precipitation, the resolution of the PET data is 30 arc seconds. The unit of this data is also in mm, and calculated over the period 1950-2000.
Land cover:
Climate Change Initiative (Land Cover Map, 2010) provided information about the land cover of Ethiopia. The spatial resolution of these maps is 300 meter, and a period of 5 years, between 2007-2012, has been taken to analyse the land of Ethiopia. The land cover data shows several different elements, and so also where croplands are. Beside this main data set, also information from the internet , like the website of Global Yield Gap Atlas (GYGA, n.d.) and Global Croplands (GFSAD, n.d.) has been used to support where crop lands are within Ethiopia.
The method to find the results of the monthly precipitation and PET was as followed. With the help of QGIS, the monthly average of the PET and precipitation of all the zones has been calculated. These values then where transported to an excel file. The excel file showed per zone and per month the average amount of precipitation or PET in mm. Then from the PET, the crop specific evapotranspiration had to be found. Which actually means the amount of water a crop needs during its growing period. To find this crop specific evapotranspiration (ETc), equation 1ad been applied (Allen, Pereira, Raes, & Smith, 1998) h. Where ETp in the formula is the PET from the QGIS data set.
〖ET〗_c= K_c x 〖ET〗_p (1)
Because the crop factor, Kc, is multiplied with the Potential evapotranspiration, it has to been assumed that there will be no crop growth limitation. The Kc depends on the type of crop, and the time period within the growing period. Appendix 3.2 shows the different growing periods within Ethiopia. For maize and sorghum, this period is from half May until October. While for barley and maize the growing period start in July, and goes on four months. This are the growing periods in the Meher season. The Belg season start in March, and goes on till April and is more useful for short-cycle crops. In this report barley and wheat, the two crops with a shorter-cycle period then maize and sorghum, will also be evaluated in the Belg season for a couple of zones in Ethiopia. Only the zones with a higher satisfaction in the Belg season than in the Meher season will be mentioned. Because the length of the growing period of wheat and barley is not two months but four months, the Belg season will continue till June. Information about the crop factor are based on information from Food and Agriculture Organization (FAO) (Crop water information) and presented in appendix 3.3. Because the PET gives monthly values, the K¬c had been convert to monthly Kc values (appendix 3.4). Finally per crop the differences between the rainfall and the ETc will be calculated. This shows in which months of the growing period there is or is not a lack of rainfall, and if the total amount of rain satisfies the crop water demand.
From the QGIS information about the cropland the majority of every zone has been identified at first. With this, an excel file shows in which zone most of the land area is covered by crops or something else. Also with the other maps from the internet, zones has been selected where croplands grown. Zones with no cropland will not be discussed within this report. Mostly there is a clear explanation for the fact that no croplands are here, due to lack of precipitation or a non-functional soil type.
Dry spell
Depended on the results of the ETc , precipitation and areas with croplands, a dry spell analysis will be introduced for a couple specific areas. A dry spell is the length of successive days when it does not rain, or the rainfall is less than 1mm (Savenije, 2007). A long dry spell, like 8 days can already lead to significant damage to crops. The length of these nagging days is also called the critical dry spell.
Probability of occurrence of a dry spell
So to determine the probability of a critical dry spell, first a dry spell analysis have to been made. A dry spell analysis shows the probability that a dry spell will occur within the taking growing period and amount of explored years. By using daily rainfall data (World Weather Online, n.d.) the spell duration(t) and frequency(i) of the spell can be found. The World Weather Online provides reliable information for over seven years. The period that will be evaluated will be the growing season of the crop. So first the dry spell length and frequencies of these diverse dry spells has to been found. Then all the results have to be enumerated, and per dry spell the accumulation (l) will be calculated. The accumulation is the number of times a dry spell take place equal or longer than the dry spell duration. The probability (p) that a dry spell occurs during the growing season can by calculated with the next equation:
p=l/N (2)
Where:
p = probability
l = accumulation of dry spell duration
N = amount of noted years * n
n = number of start days of the growing period. Which is the amount of days of the growing season plus 1 minus the spell duration.
So for n, the if the growing season is like 135 days and the length of the dry spell is 8 days, then n is 128 days. Next the probability (q) that a dry spell does not takes place during the growing season, can be found with the equation 3.
q=1-p (3)
Finally the probability(P) that a dry spell will take place at least once in the growing season, can be obtained with the final equation:
P=1-q^n (4)
The different spell durations will be plotted against the probability P, which finally shows a clear probability curve of the occurrence of a dry spell.
Critical dry spell
Next the critical dry spell will be calculated (B. M. C. Fischer, 2013). The critical dry spell is the amount of days the crop can stay alive without any rain. This is due to the fact that there is still enough water within the ground where the crop can make use of. The critical dry spell depends on the available water capacity (AWC), and daily water need of the crop. The AWC is in mm and is the maximum of water that can be stock in the soil and then be used for a growing crop. The AWC depends on the kind of soil, and is the difference between the wilting point and field capacity of a specific soil. The data for the AWC comes for Yield Gap (appendix 3.5). This site provides a map with the total soil available water capacity. This maps information already took the effective rooting depth into account, so effects of the rooting depth and soil texture do not have to be evaluated. The ETc has already been calculated per zone in the previous subchapter. The unit of the ETc is in mm/day, so the total ETc of the growing period has to be divide by the amount of days of the growing period. For the critical dry spell all the four type of crops will be analysed, with the two different growing season. The formula to finally find the critical dry spell is been given by equation 5.
n_cr=θ/〖ET〗_c. (5)
Where:
θ = Total soil available water capacity in mm
ncr = the critical dry spell in days
ETc = daily crop evapotranspiration in mm/day
Commodity prices:
Last the prices of the crops and the volatility of the prices will be included. FAOSTAT provides information about the four crops and the prices from 1994 until 2012 specific for Ethiopia (Producer prices annual, 2015). The value will be in Birr/ton. Birr is the Ethiopian unit of currency, and in 2012 one US Dollar was averagely equal to 18.2246 Ethiopian Birr. (Exchange Rates). The prices form FAOSTAT are the nominal crop prices and to compare the prices over the 18 year the real prices will be needed, so the inflation of the Birr will be left out. The value of the Consumer price index (CPI) is needed to find this real price. This index measures the fluctuation of the ‘cost of living’ between time periods. Index mundi present data of the Consumer price index of Ethiopia for 1994 until 2012 (Ethiopia consumer price index). The year 2010 will be the comparison year and will have the index value 100. To calculated the real prices equation 6 can be used:
P_(r,z)=P_(n,z)*(〖CPI〗_2010/〖CPI〗_z ) (6)
Where:
Pr,z = Real price in year z
Pn,z = Nominal price in year z
CPI2010 = Consumer price index in 2010 (=100)
CPIz = Consumer price index in year z
The real prices will only be about the prices fluctuations of the crops, and not the income so the the real price times the yield. Because the yield also depend on the use of fertilizers and the weathers.
Because this report is actually only about the four major cereal crops, only the prices will be evaluated of these four crops. But if with only these four crops no advise can be given, other crops will also be taken into account.
Analysis and results:
Because there are three method and data ways, this part will also consist of the following three analysis and results parts: precipitation, ETc and croplands; dry spell analysis and last the commodity prices results.
Precipitation, ETc and croplands
From the QGIS data the monthly PET and monthly precipitation per zone has been calculated. The figures in Appendix 4.1 and appendix 4.2 show the analysis of the precipitation and PET per month for Ethiopia.
The rainfall in Ethiopia has large variation, which can be witnessed as well in figure 4. The figure shows that most of the rain falls in the summer months: June, July and August. This happens mostly in the North West, West and centre of Ethiopia. In these areas, the dry season on the other hand is between December and February. In the Southeast, the Somali region, most of the rain falls in April, May and October, and between June and August the rainfall is significant low compared to the other regions. So there is a big different between in amount of precipitation and the period when it rains of the highlands and the lowlands. The PET demonstrate (appendix 4.2) that the highest PET values are in the lowlands, so in the Afar region and Somali region. In the highlands the evaporation values are significant lower.
Figure 3 Rainfall graphs for different locations in Ethiopia. The y-axis is the precipitation in mm, and the x-axis shows the month. The scale of all the graphs is the same, with a maximum monthly precipitation of about 300 mm.
From the PET the crop specific evapotranspiration will be calculated by multiplying the PET with the crop factor (equation 1). The crop factors per crop and per month are given in Appendix 3.4. First the ETc for the main growing season, the Meher season, will be calculated. The start date of this growing season depends on the crop. For maize and sorghum the growing season start half May, and for wheat and barley in July. After that for 6 zones also the Belg season will be taken into account, due to the fact that this season meets more the crop water demand than the Meher season. More explanation about these zones will follow later. Table 1 in Appendix 4.3 shows the ETc values per crop, per month and per zone for the Meher season. The crops with the same growing period will be placed next to each other, so maize next to sorghum and wheat next to barley.
Then appendix 4.4 finally shows for every crop the difference between the monthly rainfall and monthly ETc for all the 79 zones. The green coloured values are the months within that zone where the rainfall is higher than the crop water demand, while for the red coloured values there is a lack of rainfall. To make a clear overview, the zones will be categorized for every crop into 5 different groups (figure 4) depended on these values. First locations where there was a total lack of rain where analysed. Then the location where the precipitation was every month higher than the ETc. The remaining locations where categorized by the fact if the months with a lack of rain could by accomplished with the surplus rain of the previous month(s) or not. If this is the case, local storage will be needed to capture the surplus water. Other the locations will need an irrigation system. Finally there are a couple of locations where there are months which can be accomplished and months which cannot be accomplished. These location can make use of both irrigation and local storage. Appendix 4.5 shows a table to which group a zone belongs per crop. The names of the groups describe the rainfall and ETc differences or what zone would need to cope with the crop specific evapotranspiration.
The names and explanation of the different groups are as followed:
Total lack of rain: In the growing period, there is a total lack of precipitation. So for some months it can be that the rainfall was higher than the crop water demand, but the total sum of the months shows that the ETc is higher than the precipitation.
Every month enough rain: In every month of the growing period the amount of precipitation is enough to meet with the crop water demand.
Local storage needed: There are months within the growing period where the rainfall is less than the ETc. But this can meet in the same growing period with the help of local storages, because there is a surplus of rain in the previous months. This surplus of rain is enough to measure up with the lack of rain in the next month(s).
Irrigation needed: This is actually the group between zones where local storage is needed and where a total lack of rain is. The total sum of the months shows that the rainfall is enough for the crops, but there are months where the precipitation is lower than the rainfall. This lack cannot be completed with the surplus rainfall in the month(s) before the months with a lack of rain of the same growing period. So to complete this lack an irrigation system is needed.
Local storage and irrigation needed: This group is only notable for the crops maize and sorghum. It means that there are months which can be completed with the surplus precipitation in the previous months, but there are also months which this cannot be completed. So the local storage can be used for the surplus rain and the irrigation can give extra complement.
Appendix 4.6 displays graphs of all five the groups. This graphs make clear what the differences between the precipitation and ETc per group is. It are just general graphs for the groups They will not be crop specific, but stand for the rendition of the group.
Like already mentioned, there are six zones where most of the rain does not fall in the Meher season but in the Belg season. This are the zones with the number 31, 55, 56, 61, 62 and 63. Only the zones where there was a significant difference between the Belg and Meher season are noticed. Besides, because the Belg season is shorter than the Meher season, only the crops wheat and barley will be evaluated for these zones, because the growing period is shorter than the growing period of maize and sorghum. The colour and name of the groups from the Meher season will be the same for the Belg season. Figure 5 shows the map for wheat and barley. It is just on map, because there was no between the groups of these two crops. Appendix shows again to which group the zones belong, and to which group they belonged in the Meher season.
Cropland
Because this report is especially for the croplands, it is important to know where these croplands are. This was the last analysed from the QGIS data. First the map about the land cover with the croplands had been analysed (appendix 4.8). From this map the majority of every zone had been calculated. This shows what is above all present is the zone, and so also in which zones the crops where mostly present. Next this information has been completed by evaluating several maps of croplands in Ethiopia from the internet. Eventually, a final map is made to show where most of the croplands are, and where hardly croplands are (appendix 4.9). Only recommendation will be made for the areas with croplands. By this, only the group with significant low amount of croplands will be left out.
Dry spell analysis
Like already mentioned before, the dry spell analysis will look at a couple specific area depended on the precipitation, ETc and croplands. This locations will be as much as possible different from each other. The dry spell analysis will zoom in at specific places within the zones. The places will be taken as a represented of the zone, and other zones which belong to the same crop group mention in chapter 4.1.
The period that will be taken to calculated the dry spell is the growing period of the crop. So for maize and sorghum this is mid-May to September, and for wheat and barley July to October. In this way there will also be two probability curves. The growing periods have been analysed for seven year, which was the maximum available data that could be freely found. The places that had been analysed are as followed: Diyadib, Jimma, Abobo, Mekane Selam and Adi Eraznye. Figure shows the locations of these places in Ethiopia. The calculation for the dry spell and critical dry spell are given in equations 2 until 5. The intersection of these two curves gives per crop the probability that the crop land will fail. Because the probability of a dry spell that occurs would never get a zero probability, the amount of days for the zero probability has been estimated by adding just plus one to the longest dry spell. So a dry spell longer than that would just never happen.
Figure 6 The five location for the dry spell analysis in Ethiopia: Diyadib, Jimma, Abobo, Adi Erzanye and Mekane Selam
Diyadib
Diyadib lays within zone 78, close to the dry lowland of the region Afar. Over here every month of the growing period there is a lack of rainfall. In Diyadib the soil total AWC is about 55 mm. Per crop the daily average ETc has been calculated. The result per crop of this daily ETc value can be found in appendix 4.11. Appendix 4.11 also shows the critical dry spell in days, and the calculation for the probability of occurrence of a dry spell for Diyadib. In figure 5 the probability and critical dry spell per crop has been traced. For critical dry spell is around the 15 days. The graph clearly shows that the probability that a critical dry spell will occur, is between 65% and 75%. In this way in a time period of 7 years, the yield will only be successful for 5 year. Not to be forgotten that the rain that eventually fails within zone 78, is even not enough for the crop to grow properly.
Figure 7 Probability of dry spell and the critical dry spell in Diyadib with on the right the location of Diyadib within Ethiopia.. The right graph is for the growing period of maize and sorghum, and the left graph is for the growing period of wheat and barley. The x-axis provide the probability and the y-axis the duration. The intersection of the two curves gives the probability that the crop land would fail.
Jimma
Jimma is a kind of the opposite of Diyadib. Over here for every month the amount of rain for all the four crops is enough. The total soil AWC in Jimma is one with another 45 mm. Figure 7 shows for Jimma the probability that a dry spell will occur per growing season and the critical dry spell per crop. It shows that the curves that should intersect, do not intersect. Especially in the growing period of maize and sorghum the dry spell is pretty low, while for the growing period of wheat and barley there are periods with longer dry spells. Still there are no intersection between the curves. Appendix 4.12 presents the calculation of the dry spell, and the critical dry spell and ETc values per crop.
Abobo
Abobo is a city in the South West of Ethiopia, and lays in zone 23. In zone 23 the amount of rain is in total enough for the four crops, but there are months with a lack of rain. This lack can be completed with the surplus of rain from the previous months with the help of local storage. The total soil AWC in Abobo is about 55 mm. The dry spell probability and critical cry spell (figure 8) show that there are no intersection between the curves, so the probability that the cropland would fail is zero. Which assumes that within the months with a lack of rain there are no long dry spell periods, but the amount of rain that fails is just not enough. The calculations of the dry spell and critical dry spell can be found in appendix 4.13.
Mekane Selam
Mekane Selam is a city in the Debub Wollo zone, zone 11. In zone 11 maize, wheat and barley belong to group where a local storage will be needed, so the lack of rain can be completed with the surplus of rain in the previous months. Which is actually the same as in Aboba. But only sorghum belongs to the group that needed irrigation because there is a lack of rain in May and June, which cannot be completed within the same growing period which start mid-May. The total soil AWC is about 50 mm in Mekane Selam. Appendix 4.14 gives the daily water demand values for zone 11, and also the calculated critical dry spell and dry spell probability. The results from these calculations are given in figure 9. The dry spell analysis looks a little bit like the dry spell analysis of Diyadib, and not like the one in Abobo. Sorghum has the highest probability , which is about 75%. For maize, wheat and barley the probability that the critical dry spell would occur is around the 60%. This shows pretty much differences compared to the dry spell analyses in Abobo. Because the values of the monthly precipitation and ETc is the average over about 50 years, it can be that there are years with a lot of rain and years with a lack of rain. Aspecially because for maize the lack of rain is only 14.7 mm. Looking at the ETc value, this would be only a dry spell of 5 days, and is much lower than critical dry spell. So it can be assumed that in Mekane Selam there are years with a lot of rain and years with a lack of rain, but averagely enough with the help of a local storage for the crops maize, wheat and barley to grow properly. .
Adi Erzanye
Adi Erzanye lays in the extreme North-West of Ethiopia, within zone 77. The precipitation in zone 77 is enough for maize, but for sorghum there is a lack of rain within the first two month. While for wheat and barley the amount of rain is not enough in the last two months. This lack can be filled with the remainder rain within the first two months. The soil total AWC is about 50 mm around Adi Erzanye. Appendix 4.15 shows the ETc values per crop and the calculated critical dry spell. Also the calculations of the dry spell probability has been noticed over here. The total results are displayed in figure 10. For maize and sorghum there are no intersections of the probability of the dry spell and the critical dry spell. While the critical dry spell of wheat and barley does intersect with their associated probability curve. This intersect is around 35% and 40%. With the fact that there was a lack of rain within the last two months, this result is not out of line.
Figure 10 Probability of dry spell and the critical dry spell in Adi Erzanye, with on the right side the location of Adi Erzanye in Ethiopia. One graph is for the growing period of maize and sorghum, and the other graphs is for the growing period of wheat and barley. The x-axis provide the probability and the y-axis the duration. The instersection of the two curves gives the probability that the crop land would fail.
All the five places show different results. What should be noticed is that the dry spell only provides information about the probability that there would be a long dry spell and the chance that the cropland will fail, while the previous subchapter provided more information about if the rainfall within the different zones is even enough. With the different dry spell analysis and the previous subchapter recommendation can be made for particular locations.
Commodity prices
FAOSTAT only provided data until 2012 for Ethiopia. Appendix A 4.15 shows the nominal prices for the four crops between 1994 and 2012. From 1994 to 2006 the nominal prices were around the same level. But from 2006 to 2008 the nominal prices increased rapidly. The table with the Consumer price index is given in appendix 4.16. Then using equation 6 will give the real prices of the crops (figure 11). The fluctuations of the real prices between the four crops are slightly the same. What is mostly notable is again the price change between 2006 and 2008. This is due to government’s policy action with lead to food price hikes (Rashid S. , 2010). From 2006 to 2007 the real price increased more than 50%. After this pretty fast real price increase, the real crop prices decreased until 2011. The maximum price drop in this period was about 20% in one year, where for maize was the highest price drop of 27% in just one year.
Irrigated zones
Because the total amount of rain in the growing season is not even enough to satisfy the water demand of the crops, an irrigation system is needed within this area. Costs for an irrigation system really difference per zone and depends on the amount of water that is extra needed and how easily the access to the water for irrigation is. These water accesses can be from the groundwater through wells or from surface water. If the access to water is facile the prices for the irrigation system will be lower, just like the labour costs. From the dry spell analyses in Diyadib the probability that the cropland would fail was pretty high, which does make sense because the amount of rain was not even enough for the water demand of the crops. A weather index insurance would also be useful to have a resilience against big shocks of the climate change. The R4 Rural Resilience Initiative (R4) already provides insurance for over 240000 small farmers in Ethiopia and regards the food security and resilience by risk management. The aim of R4 is to decrease the shocks of the climate change, and improve the food security and resilience to the climate change. In a good crop year the small farmers pay the company, while in a bad year they get payed back. Which this the small farmers will also build up a financial base for the a more food security of the rest of their lives. (R4 Rural Resilience Initiative, 2014). The R4 is not only active in Ethiopia but also African countries like Senegal, Zambia and Malawi. With this fact, the mutuality of the insurance would work and also because the location within Ethiopia have different rain seasons(Belg and Meher). With this initiative in extreme drought the people in these zones while get payed back and do not have to pay higher cost for the irrigation system. The amount of money the farmers have to pay and get depend on the weather indices and how extreme the drought is.
Every month enough rain for all four crops
In these zones the rainfall was every month and for every crop enough. Also the dry spell analysis in Jimma (figure 7) displayed that the chance that a dry spell occurs within the seven years is zero. So actually, based on these information, there are no hydrological or financial instruments needed.
Local storage needed for all four crops
In these two zones for all the four crops the amount of rainfall was not enough in every month. There was one month with a lack of rain, but this month can be filled with the surplus rainfall of the previous month(s). This can be done by harvesting the surplus water and, like a name already suggest, collect it in a local storage or reservoir. A local storage with a geo membrane plastic sheet would cost about 767 ETB (Albert Tuinhof, 2012) and the life expectation of this kind of reservoir is 10 years. The storage capacity of the reservoir will then be about 150 m3. Table 1 shows the average yield of crops (Production crop, 2015) and prices in 2012 to show what the average income of a small farmer per acre is. Comparing these prices with the cost of the reservoir divines that the cost of the reservoir are acceptable. The dry spell analysis that fits with this specific location is the analysis in Abobo. Which actually shows, like the dry spell analysis in Jimma, that for these seven years there is no probability that the cropland would fail. So just a local storage would be fine for these zones.
Ton/ha(2012) Birr/tonn(2012) Birr/ha
Maize 3.06 4200 12852
Sorghum 2.11 5620 11858
Barley 1.75 6052 10591
Wheat 2.11 7200 15192
Table 1 Yield and cost of maize, sorghum, wheat and barley in 2012. Multiplying the yield with the real prices gives the income in 2012. Source: FAOSTAT (Production crop, 2015)
Commodity prices
Then last, also the price fluctuations in Ethiopia has been evaluated. The price fluctuations are for the four analysed crops the same.. If there would be differences between fluctuations of the crops, mutual insurance could be easily adopted. But over here, there is no variation within the real prices and now the insurance company will make or profit or loss. So four these four analysed crops mutual insurance cannot be used.
But these four crops are of course not the only crops that are on the market. So if also other crops, like pulses and fruits are taken into account, there are difference in the price Because the yield also really depend on the weather influences for this part only the real prices will be evaluated. In this way a mutual insurance would work. An insurance like an Income Stability Tool (IST) could be applied then (Farrell, 2016). With an IST the average real prices over the last 5 year will be calculated to predict the real price for the next year. Depended on this price the farmer will have to pay the insurance company, or get an amount of money. Because the real prices are in Birr/ton, this amount of money will also depend on the production of the small farmer. Last, this kind on insurance will only depend on the fluctuations in the real prices.