Analysis of climate change and land use/cover impacts on Fogera cattle population dynamics at Metekel Ranch, Northwestern Ethiopia

Michael Abera^1,2, Mitiku Eshetu³, Yesihak Yusuf Mummed³, Fabio Pilla⁴ and Zewdu Wondifraw²

¹ Africa Center of Excellence for Climate Smart Agriculture and Biodiversity Conservation, Haramaya University, P o box 138, Dire Dawa, Haramaya, Ethiopia
michaelabera76@gmail.com
² Department of Animal Sciences, Debre Markos University, P O box, 269, Debre Markos, Ethiopia
³ School of Animal and Range Sciences, Haramaya University, P O box 138, Dire Dawa, Haramaya, Ethiopia
⁴ Department of Agriculture Environment and Food, University of Molise Via Francesco De Sanctis s.n.c. 86100 Campobasso, Italy

Abstract

This study aimed to assess the impacts of climate change and land use land cover changes (LULC) on Fogera cattle population dynamics at Metekel Ranch. For this study Landsat images of 1986, 2000, and 2019 were used to generate LULC of the study area. For LULC classifications supervised image classification using the maximum likelihood classifier in ERDAS imagine software was used. A total of 303 samples were collected using a global positioning system (GPS) and used for accuracy assessment. For climate change analysis the rainfall and temperature data were collected from the national meteorological agency for the study period (1986 to 2019). Data for Fogera cattle numbers were taken from the herd book of the ranch. Likewise, pasture land data was taken for grazing land obtained from the classified images of these years. Then, climate change, and Fogera cattle population dynamics trend analysis were done using the Mann-Kendall (MK) trend test as implemented in R-package. To check the variability of annual rainfall standardized anomaly index was calculated. The result revealed that between 1986 and 2019 there was the expansion of farmland and forestland, which have increased by about 34.8 and 0.4 ha, respectively. However, the area covered by grazing land and shrub has shown a decreasing trend over the entire study period with a rate of -13.7 and -21.5 ha, respectively. The decrease in grazing land area could be associated with an increase in farmland and forest land cover. The MK test result showed that the annual rainfall was significantly decreasing while the mean annual temperature trend was increasing in the study area for the considered periods. The inter-annual variation of rainfall during the analysis period was 55.88%, which implies that the ranch was in non-equilibrium condition. The trend analysis result showed that the Fogera cattle population was significantly increasing whereas, the pasture land was decreasing during the study period. The increase in Fogera cattle population from 1986 to 2000 was due to an increase in pasture land which increased the forage production potential of the ranch. However, the cattle numbers declined from 2000 to 2019 due to a reduction in pasture land. Moreover, the overall stocking rate for grazing land was also increasing during 1986-2019, with an annual change rate of 0.8%. The increase in Fogera cattle population and the decline of pasture land highlight the need for intensification of cattle keeping, and matching cattle numbers with the pasture land available.

Keywords: farm land, forest land, grazing land, Mann-Kendall test, trend analysis

Introduction

Livestock systems in developing countries are characterized by rapid change, driven by factors such as population growth, increases in the demand for livestock products as incomes rise, and urbanization (Delgado et al 2001; Thornton et al 2007). Livestock currently contributes about 30 percent of the agricultural gross domestic product (GDP) in developing countries, with a projected increase to about 40 percent by 2030 (FAO 2010), and is becoming the fastest-growing sub-sector of agriculture (Delgado 2005; FAO 2009). Livestock is an important component of nearly all farming systems in Ethiopia and provides draught power, milk, meat, manure, hides, skins, and other products (Funk et al 2012). Currently, the population of livestock found in Ethiopia is estimated to be 70 million cattle, 42.9 million sheep, and 52.5 million goats (CSA 2021). Livestock form an important sub-sector whereby cattle contribute significantly to food security and rural family income from sales of animals and their products. However, the country has suffered from climatic variability and extremes (NMA 2007; Alebachew and Woldeamlak 2011).

Climate-related hazards in Ethiopia include drought, floods, heavy rains, strong winds, frost, high temperatures, and lightning (NMA 2007). A consequence of the long-term climate-related changes in precipitation patterns, rainfall variability, and temperature has increased the frequency of droughts and floods (NMA 2007; Khan et al 2010). Climate change influences are more severely felt by poor people who rely heavily on the natural resource base for their livelihoods (Parry et al 2007; IFAD 2009). Of course, pastoral communities are the most vulnerable communities (Lautze et al 2003; IFAD 2009). It has been suggested that Ethiopia might see greater climate variability and extreme events in the coming decades (Parry et al 2007; NMA 2007). Climate change is also affecting the dynamics of the livestock sector (Hoffmann 2010; Thornton and Gerber 2010). Studies had reported that there are correlations between rainfall variability and livestock population dynamics (Solomon and Coppock 2002; Kgosikoma 2006; Ayana 2011). Rainfall variability greatly influenced herd dynamics in terms of herd die-off and lower birth rates, which also considerably affected milk production for household consumption (Solomon and Coppock 2002; Ayana 2011). In addition, land-use/cover changes (LULC) exacerbate the existing management challenges as they affect cattle production through the decline of pasture areas caused by expansion and intensification of crop production systems as well as urbanization. The decline of water and pasture quality from the effects of climate variability and change also affects cattle production. LULC resulted from deforestation which affects climate variability and can change rainfall distribution and pattern, and hence water availability for livestock production. Global climate change is expected to increase these problems (Asner et al 2004).

Land cover alterations are caused by poor management of land resources which lead to severe environmental problems. To understand situations of unrecorded land-use change, the interpretation of data from earth-sensing satellites has become vital. The LULC changes can be monitored at different spatiotemporal scales using geographic information system (GIS) and remote sensing (RS) tools that provide scientific procedures to analyze the pattern, rate, and trend of environmental change at all scales (Wilkie et al 1996; Dezso et al 2005; Kolios and Stylios 2013). Satellite images are the most common data source for change detection, quantification, and mapping of land cover patterns due to its repetitive data acquisition and availability of accurate geo-referencing procedures (Abd El-Kawy et al 2011). The series of published evidence indicates that the harmful effects of climate change, when taken as a whole, are likely to be significant and expected to increase over time (NASA 2013; Oki et al 2013). The explicit spatial knowledge on the changes in LULC that are modulating the climate system through sinking and emission of atmospheric carbon dioxide for improved cattle production is needed. This knowledge can be used to guide policymakers, managers, and planners to assess and respond to the challenges that climate change would impose on agricultural water management, cattle production, and food security (Turral et al 2011). Indeed, the effect of climate changes and LULC on ruminant livestock population dynamics was not fully investigated and analyzed in Ethiopia. Consequently, awareness creations on the effects of climate change on ruminant livestock population dynamics can provide appropriate management practices which enable to cope with the problems (Kefyalew and Tegegn 2012). Thus, this study was conducted to assess the impacts of climate and LULC on Fogera cattle population dynamics at the Metekel Ranch of Awi Zone. Specifically, LULC was assessed using remote sensing and geographic information system technologies for the periods of 1986–2000, 2000–2019, and 1986-2019 and analyzed the trends in Fogera cattle population dynamics, rainfall, temperature, and pasture (in the form of land cover) for these periods.

Material and methods

Description of the study area

The study was conducted in Metekel ranch which is located in Guangua district of Awi zone in Amhara National Regional State, Ethiopia and is situated at about 505 km North-west of Addis Ababa, and 200 km from the regional town Bahir Dar on the road to Guba. Metekel ranch is located at 10°57’6.5232” N and 36°30’45.0864” E. Its altitude ranges from 1500-1680 meters above sea level (m.a.s.l). The ranch was established in 1986 for the Fogera cattle conservation and improvement program. The vegetation is mostly composed of perennial and annual grasses and a few scattered trees. The annual mean relative humidity (RH) is 61.7% and it reaches high from June to October (76.7-83.8%). The ranch receives an average annual rainfall of 1730 mm and the average temperature ranges from 13.7 to 29.5°C (ENMA 2010). Rainfall distribution is bi-modal. According to Ababa (2007), the study area has three seasons classified as the dry season (October-January), short rainy season (February-May), and long rainy seasons (June-September).

Figure 1. Map of the study area

Data collection

Land use/cover change analysis

Land use/cover change (LULC) analysis was conducted using time-series satellite imageries downloaded from the USGS website (https:/glovis.usgs.gov/). Landsat images of 1986, 2000, and 2019 with 170 paths and 52 rows were used to generate LULC (Table 1). The period 1986–2019 (34 years) depicts the different levels of human and developmental interventions as well as the climate variability that may affect Fogera cattle population dynamics within the ranch. Cloud-free images or images with limited clouds (less than 5% clouds) were used. All images were acquired in the dry season to avoid the effects of seasonality. The analysis was conducted in three different stages such as pre-processing, image classification, and post-processing. A total of 303 samples were taken for validation or accuracy assessment using Global Positioning System (GPS). A maximum likelihood classifier algorithm was used to classify the images. Contrast enhancement of color composite image was done. In the third stage, the classified images were checked for accuracy in ERDAS imagine 9.1 software. The accuracy of classification was assessed by comparing the classified images with GPS points collected for ground-truthing. Moreover, a digital camera was used to record photos of physical features in the respective LULC types. Post-Classification Comparison (PCC) based on supervised classification of multi-temporal images were used for change detection assessment. It provides information on the transition from one land use/cover class to another (Pacifici 2007). The rate of change was calculated for each LULC class as the rate of change (ha/year) (Abate and Singh 2011) shown in equation (1):

where: A = Recent area of the land use and land cover in ha, B = Previous area of the land use and land cover in ha, and C = Time interval between A and B in years.

Rainfall and temperature

The rainfall and temperature data were collected from the national meteorological agency, Chagni weather station located nearer to the ranch. The rainfall and temperature trend analysis was conducted using the Mann-Kendall (MK) trend test (Mann 1945) and (Sen 1968), as implemented in R-package version 3.5.2 program.

The Mann-Kendall test (Mann 1945) and (Sen 1968) was applied using the formula:

Where n = number of data points, x_k and x_j = data values in time series k and j (j>k), and sgn(x_j= x _k) is defined as:

The variance of S was calculated as:

Where q = the number of tied groups and tp = the number of data points in the p^th group.

Whereas the values of S and VAR(S) are used to compute the test statistic Z as follows:

Where, the positive values of Z indicate upward (increasing) trends in time series, and the negative values show downward (decreasing) trends. Trends are then tested against some critical values (Z_1−α) to show that either they are statistically significant or not. For example, if |Z| > Z_1−α, (e.g., Z_1−α at α = 0.05); the null hypothesis of no-trend is rejected, and the alternative hypothesis of significant trend is accepted. The standardized anomaly index (SAI) was calculated for annual mean rainfall and presented graphically to evaluate the inter-annual fluctuations (variability) in rainfall in the study area for the period 1986 to 2019. Thus, it was used to examine the frequency and severity of drought events and analyzed with the formula given by Koudahe et al (2017).

Where; P_t = annual rainfall in year t; P_m = long-term annual mean rainfall throughout the study period and σ = standard deviation of annual rainfall throughout the study period. Thus, positive normalized rainfall anomalies indicated greater than long-term mean rainfall whereas negative anomalies indicate less than the mean rainfall.

Pasture and Fogera cattle numbers

The data for Fogera cattle numbers were taken from the herd book of Metekel ranch for the study periods (1986-2019). Grazing land is the main source of pasture for Fogera cattle in the study area. Thus, pasture land data was taken for grazing land obtained from the classified images of 1986, 2000, and 2019 following Nkya et al (2017). The classified images were used as subsets of the ranch images to obtain their corresponding values for the pasture vegetation areas. Then, the rate of change (cattle/year) for Fogera cattle numbers was calculated for 1986, 2000, and 2019. Likewise, the rate of change for grazing land (ha/year) was calculated for the classified images of 1986, 2000, and 2019. The classified images were used as subsets of the ranch images to obtain their corresponding values for the pasture vegetation areas.

Dry matter production from different land types

The quantity of feed DM obtained annually from different land-use types was calculated by multiplying the hectare of land under each land-use type by its conversion factors. Conversion factors of 2.0, 1.6, 0.7, and 0.2 tDM/ha/year were used for open grazing land, shrubland, forestland, and cropland, respectively (FAO 1984; FAO 1987; FAO 1988).

Estimation of balance between feed supply and demand

The concept of a tropical livestock unit (TLU) was used to compute the stocking rate of the ranch area. The TLU was used to convert all livestock types into a common denominator using the conversion factor of 0.7 for a cow in the herd, 0.1 for sheep, 0.08 for goat, and 1.25 for a camel. The number of Fogera cattle population in the ranch was converted into tropical livestock units (TLUs) using the approach in Varvikko et al (1993) for comparison purposes. Total available dry matter per year from grazing land was compared to the annual dry matter requirements of the cattle population in the ranch. The DM requirement of the cattle population was calculated based on the daily DM requirement of 250 kg dual-purpose tropical cattle (an equivalent of one TLU) for maintenance requirement that needs 6.25kg/day/animal or 2281kg/year/animal (Jahnke 1982). Moreover, McMeekan (1961) also stated that the stocking rate was expressed simply as cows per hectare and used as a simple measure of the ratio between feed demand and feed supply. The number of cows gives a measure of the annual feed demand, while a hectare provides a measure of the amount of feed (pasture) available. Once the total forage supply and forage demand were estimated, the stocking rate was determined by the formula:

Results and Discussions

Land use/cover change detection at Metekel ranch (1986-2019)

The ranch managers and key informant interview before the study commenced indicated that the total land area of the ranch during the establishment period in 1986 was 6200 ha. However, our analysis result using map digitalization techniques showed that the total area under the ranch was 4456 ha. The disparity in these numbers might be due to a lack of clear demarcation of the ranch boundaries to be captured through satellite images. Moreover, the variation in these numbers might be due to the lack of a true shapefile for the ranch area. The study showed that the dominant LULC during the entire period was grazing land, followed by shrubland cover (Figure 2). Thus, this result suggests that the major feed resource for Fogera cattle in the ranch is coming from grazing land than other land cover types. Our study showed the area under farmland, and forest showed positive changes. Between 1986 and 2019 the expansion of farmland and forest has increased by about 34.8 and 0.4 ha, respectively. On the other hand, the area covered by grazing land and shrub has shown a progressive decreasing trend over the entire period with a rate of -13.7 and -21.5 ha, respectively. Likewise, the group discussion held with ranch managers also supported our finding, in which the observed decline in the LULC was attributed to the expansion of farmland consistent with the result obtained from the image analysis. The main reason for the expansion of farmland in the area was due to the establishment of a new seed multiplication center in the grazing area in 2010, subsequently, most of the grazing land was converted to farmland. Thus, they indicated that in 2010 most of the grazing land of the ranch was shifted in to crop seed multiplication center for the regional government which is also confirmed with image analysis results. Moreover, the ranch managers also reported that the increase in forest land was due to the planting of fodder trees such as tree lucerne for livestock feed resources.

In 1986 of the highest area coverage was covered by grazing land 50.25%, while land cover types such as forest, shrub, and farmland accounted for about 20.39%, 29.33%, and 0%, respectively. In the year 2000 the area coverage by grazing and farmland increased to 54.42%, and 0.72 %, while the area coverage by forest and shrubland in the same year decreased to 15.86%, and 28.97%, respectively (Table 2). In the year 2019, the coverage of farmland and forest increased to 26.57% and 20.71% while the coverage of grazing land and shrubland decreased to 39.81%, and 12.90%, respectively. The analysis of LULC found that the farmland expanded rapidly at the expense of grazing land and shrubland over the last 34 years in the study area. Our result also showed the overall accuracy of image classification and kappa statistics of 85.15 % and 80%, respectively. Kappa statistics ranged from good to excellent, and thus Kappa greater than 75% indicate strong agreement (Maingi et al 2002).

Figure 2. Land use/cover change analysis at Metekel ranch (1986-2019)

Farm/cultivated land cover

The study showed the increasing patterns of farmland coverage over the analysis period in the study areas (Figure 3). Farmland cover increased from nearly 0% in 1986 to 26.59% in 2019 with the rate of 2.1 ha per year. Moreover, the study indicated that the farmland cover trend showed a more likely change between 2000 and 2019 with a change rate of 60.6 ha per year. The increasing trends of farmland could be associated with steadily declining patterns of grazing land and shrubland cover. Furthermore, the result of this study showed that there was an increase in farmland cover during the entire study period, with an annual average change of 34.8 ha per year. Even though the objective of the ranch was intended for the conservation and improvement of the Fogera cattle breed, the main reasons for the reduction in grazing and shrublands in the ranch area was since 2010 the regional government intentionally forced to use almost about 2000ha of land for improved seed varieties multiplication center. This was due to the scarcity of land in the ranch area for crop production. On top of this, crop production is the stop priority over livestock production for communities surrounding the ranch area. Thus, the government was forced to use part of the ranch area for seed multiplication centers and distribution for communities. Furthermore, even though the grazing land was reduced while the farmland expanded in 2010, the government was planned that the crop aftermath remaining from the multiplication of improved seed varieties were to be used as cattle feed resource. In line with this finding Matiwos et al (2020) reported an increased cropland cover by 8.98 % over the analysis period (1986-2018) in the southeastern rangeland of Ethiopia. Likewise, Aklilu et al (2014) reported an increased cropland cover by 8.3% over the analysis period (1973-2007) at Liban district of southeastern parts of Ethiopia. Getachew et al (2010), also observed that cultivable land was showed a rapid increase since 1987 in southern Ethiopian rangeland.

Figure 3. Land use land cover types of Metekel ranch (1986-2019)

Grazing land

Grazing land (Photo 1a) is the primary feed source for Fogera cattle in the study area. The grazing land in the study area showed a consistent declining trend throughout the study period. Its area decreased from 50.25% (2239 ha) in 1986 to 39.81% (1774 ha) in 2019. The decreasing trends of grazing (Figure 4a) and shrubland (Photo 1b) cover could be associated with an increasing trend of farmland (Photo 4) and forest land (Photo 1c) cover. In agreement with the present finding Abyot et al (2014) reported an increased rate of agricultural land cover over grazing land in the Banja district of Ethiopia. Similarly, previous studies by Berhan and Woldeamlak (2014) and Aklilu et al (2014) reported consistently declining patterns of grassland during the analysis period of 1973-2007. Moreover, Getachew et al (2010) also reported steadily declining patterns of grassland cover from 1967 to 2002. This could result in a severe animal feed shortage and affect Fogera cattle productivity and population dynamics. Therefore, this can be generalized that the decline in birth weight (BW) and weaning weight (WW) of calves, would be highly associated with the decline in grazing land of the ranch.

Photo 1. Grazing (a), shrub/bush (b) and forest land (c) cover at Metekel ranch (1986-2019)

Trends of climate change and Fogera cattle population dynamics

Temperature and rainfall trends

The Mann–Kendall test result showed that the annual rainfall trend was decreased (p<0.01) while the mean annual temperature trend was considerably increased (p<0.0001) at Metekel ranch over the last 34 years (1986 to 2019) (Table 3). The decrease in the rainfall could be attributed to the decline in the forage production in the ranch, subsequently, this had a direct impact on the production and productivity as well as population dynamics of Fogera cattle. This result is corroborated with the report of Nkya et al (2017), who observed an increase in beef cattle numbers and the decline of pasture in Tanzania. The standardized anomaly index (SAI) revealed that the number of years that hand records of rainfall below the long-term average at Metekel ranch were about 55.88% (Figure 4). Moreover, the study showed that the frequency of low rainfall amount increased in the last decade. In this regard, greater than 50-60% of the years in the last decade had a record of annual rainfall below the long-term average in the study area (Figure 4). Michael et al. (2020) also reported an increase in the temperature and decrease rainfall amount in the study area which is in line with the current result. Thus, the study suggested the need for appropriate amelioration practices to alleviate the impact of the drought effect resulting from climate change at Metekel ranch. Similarly, Michael et al (2020) reported regular prediction of climate change and variability and designing pertinent response strategies is essential to reduce the adverse impacts of climate change in the Awi zone, northwestern Ethiopia.

Figure 4. Standardized anomaly index for rainfall at Metekel ranch (1986-2019)

Fogera cattle population dynamics

Even though there were fluctuations in cattle numbers, the overall assessment of this study showed that the Fogera cattle population in the ranch was increasing during the study period (1986-2019) ( P<0.0001). However, it indicated that there was a decrease in pasture land by -13.68 ha per year between 1986 and 2019 whereas; the cattle population was increased by 118 cattle per year between 1986 and 2000. On the other hand, the cattle population was declined by -38 cattle per year between 2000 and 2019. The increase in Fogera cattle population from 1986 to 2000 was due to an increase in pasture land which might increase the forage production potential of the ranch. According to the group discussion report, the increase in pasture land during this period was due to the change of some shrublands to grazing lands which is also consistent with the LULC image analysis result. However, the declined cattle population between 2000 to 2019 was due to the reduction in pasture land as most of the ranch area was changed into farmland in this period. Thus, the ranch sold the cattle as coping strategies to the shortage of feed resources. This was in response to pressures on land use and climate change impacts such as the decline in the pasture. Furthermore, the decrease in the pasture land while an increase in cattle number indicates that animals would be affected in the coming years because of the feed shortage in the ranch.

Cattle keeping in the ranch is still predominantly the extensive method, and there is a large shift between the increased Fogera cattle numbers and pasture. Following this, the birth and weaning weight are very low in this ranch which leads to lightweight cattle. The decline of pasture land cover could also contribute to climate change and variability, as vegetation regulates climate by affecting the amount of carbon dioxide in the atmosphere. The increase in Fogera cattle numbers and the decline of pasture highlight the need for intensification of cattle keeping, and matching cattle numbers with the pasture to prevent deterioration in pasture quality and quantity. The previous study in Tanzania also showed that an increase in cropland resulted in a decline in beef cattle production in Magu District for the years 1980, 2000, and 2010 which is consistent with this study (Nkya et al 2017).

Stocking rate

The overall stocking rate (SR) of the ranch showed that an increasing pattern except for forest land in the study area (Table 4). The overall SR of the ranch was 1.73 TLU/ha/year in 1986, which is comparable with the value (1.8 TLU/ha) reported in the southeastern rangeland of Ethiopia (Matiwos et al 2020). However, the total SR of the ranch during 2000 (2.06 TLU/ha) was much higher than the optimum stocking rate for cattle (0.21 AU/acre/year) reported by Thorne and Stevenson (2007). However, lower than the value reported by Matiwos et al (2020) during the 1987-1995 analysis period. The overall stocking rate of the ranch was about 2.23TLU/ha/year during the 2019 analysis period, which is also higher than the optimum value reported by Thorne and Stevenson (2007). The estimated SR for grassland in this study was significantly higher during the 2000-2019 analysis period with an increasing rate of 36.7%. Moreover, the overall SR for grazing land was also increasing during the 1986-2019 analysis period, with an annual change rate of 0.8%. The increase in SR with decreasing grazing land has a significant effect on animal performance. Similarly, Rouquette et al (2020 reported that increasing the SR to 2.1 head/acre and 3.0 head/acre resulted in reduced average daily gain (ADGs) of 2.21 and 1.13 lb/day, respectively in beef cattle in the Middle South. Furthermore, a previous study indicated that long-term, sustained animal production, and profits occur halfway between maximum animal production per acre and the point at which individual animal performance begins to decline (Hart et al 1988). Thorne and Stevenson (2007) also reported that at low SR, individual animal performance is maximized because animals are free to select high-quality forage. The general SR of shrubland was also significantly increasing during the analysis period of 2019-1986 with an annual change rate of 3.7%. However, the overall SR for forest land was decreasing during 1986-2019, with an annual change rate of ~0%. This study further confirmed that the decrease in BW and WW observed in previous studies by Abera et al 2021a, b might be also because of the higher SR of the ranch. Furthermore, on top of the higher SR observed in this study, the variability of rainfall was another factor that affected the forage production optional of the ranch.

Conclusion

• There is clear evidence that the increase of anthropogenic activities such as increased commercial agriculture practice has resulted in the decline of pasture in Metekel ranch. This infers land use land cover change has significantly affected the production and productivity of Fogera cattle in the study area.
  
  
• On top of land use/cover change, climate change has a great role in determining the livestock feed availability in the ranch. Therefore, planting indigenous fodder trees species for livestock feed resources could be promising as they have also important for maintaining the climate change effect.
  
  
• The study also revealed that the area under grazing land is decreased while the stocking rate was increasing at the same time. Therefore, it can be recommended that intensification of the current extensive cattle production systems, forage conservation, and matching of cattle numbers with the pasture available is a prerequisite for the sustainability of the ranch under the changing climate.
  
  
• This study also showed that remote sensing data can be effective and efficient in the monitoring of pasture land in the future change, hence this data can be used to support the range managers to take immediate action on ranch conservation and the pasture land in the particular area.

Acknowledgments

The authors are grateful to the Africa Centre of Excellence for Climate Smart and Biodiversity Conservation for funding this research as part of the first author Ph.D. study. We would also like to acknowledge Debre Markos University and the Ministry of Science and Higher Education (MOSHE) for their support to undertake this study. National Meteorological Agency is also acknowledged for providing climate data. Moreover, the authors would also like to thank Mr. Seid Yimer and Mr. Elias Cherenet for their assistance on spatial data analysis of some parameters of this research. Finally, the authors would like to show their gratitude to Metekel ranch Managers, Livestock Experts for sharing their pearls of wisdom during field work.

Data availability

The authors declare that all data supporting the findings of this study are available within the article and its supplementary information file. However, the land use/cover change dataset analyzed during this study were derived from the earth explorer domain of the Landsat program (https://earthexplorer.usgs.gov/)

Competing interests

No potential conflicts of interest to declare

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