The objective of this study was to analyze the combined effects of
climate and land use/cover changes on livestock feed resources and
livestock species composition. The land use/cover data were generated
from Landsat images of 1986, 1995, 2010 and 2018. The images were
classified using a maximum likelihood classifier algorithm. The result
from meteorological data and the land use/cover change were compared
with household perception on livestock feed availability and livestock
species composition. The result showed that the dominant land use/cover
in the eco-region throughout the study period was bush/shrub cover
followed by woody vegetation. This study revealed transition of land
use/cover from grassland, woody and forest vegetation cover to
bush/shrub and cropland in the study areas. The analysis of time series
meteorological data showed significantly increasing patterns of
temperature, and the highly variable nature of rainfall during
1986-2018. The pattern of livestock population throughout the analysis
period exhibited a significantly increasing trend. The land use/cover,
temperature and rainfall significantly affected livestock feed
availability and quality. Over the last 32 years, potential grazing
resources had been declined with a resultant increase in the proportion
of bush/shrub feed resources available for livestock. The inter-annual
variation of rainfall during the analysis period was 40.45%, which
implies that the rangeland is in non-equilibrium dynamics. The
rangelands carrying capacity was significantly decreased from 3.76
TLU/ha/year in 1986 to 1.74 TLU/ha/year in 2018. However, the stocking
rate was increased from 1.8 TLU/ha/year in 1986 into 7.15 TLU/ha/year
during 1986-2018. It is recommendable to choose camel and goat more
likely than cattle and sheep raising with increasing temperature and
decreasing pattern of rainfall that favour bush/shrub feed resources.
Hence, available feed resources and the probability of choosing
livestock species vary with eco-region and land use/cover that indicates
the need for site-specific feed and rangeland management scheme.
Keyword: Land use/cover; Feed resource; Livestock; Climate;
Rangeland
INTRODUCTION
Climate and land use/cover change have strong interconnection and the
concurrent mutual influence on each other
(Dale, 1997;
Fan, Ma, Yang, Han, & Mahmood, 2015).
According to Fahey, Doherty, Hibbard,
Romanou, and Taylor (2017), land-use changes such as deforestation and
soil cultivation changes the surface brightness. The positive radiative
forcing (lower land surface albedo) can produce through the abandonment
of pasture and forest cover. Research findings indicate that conversion
of land use/cover to agriculture is a crucial driver of climate change.
For instance, agricultural activities (crop with livestock) carried out
in the field directly accounted for 13.5% of GHG emission. Whereas
clearing forest for agriculture roughly accounted for an additional 17%
of global GHG emission (Faurès et al.,
2013). Furthermore, conversions of pastureland/ native vegetation to
cropland in semi-arid environments decreased soil organic carbon stock
by 30% within five years of cultivations
(Lipper, McCarthy, Zilberman, Asfaw, &
Branca, 2017). Conversion of tropical forest to agriculture accounts
for more than 60% of soil carbon loss
(Eshetu & Hailu, 2020).
Land use/cover changes are mainly caused by human activities including
overgrazing, expansion of built-up, forest clearing and cultivation
(Feddema et al., 2005;
Garedew, Sandewall, Söderberg, &
Campbell, 2009; Homewood et al., 2001;
IPCC, 2019;
Reid et al., 2000). According to
Benin, Ehui, and Pender (2003) and
IPCC (2019), changes in land use/cover
patterns and climate variabilities are primarily changing livestock feed
resources in terms of composition, availability and quality. Under the
dynamics of land use/cover changes and climate variability, the
livelihood of pastoral communities is under threat
(Elias, 2008;
Gebru, Desta, & Coppock, 2003;
Müller, Linstädter, Frank, Bollig, &
Wissel, 2007). Furthermore, Benin et al.
(2003) and Aklilu, Gerard, Kindie,
Lisanework, and Duncan (2014) revealed declining patterns of grazing
feed resources due to the combined effects of land use/cover and climate
change. Moreover, Wolde-Georgis, Aweke,
and Hagos (2000), Hidosa and Guyo (2017)
and Husein (2018) reported that the
quality and availability of natural pastures are considerably affected
by continuous land use/cover and climate change. The supply of quality
feed with the required quantity determines livestock productivity
(Tolera, Yami, & Alemu, 2012). In
Ethiopia, livestock feed resources are mainly derived from foraging over
large areas of grazing and browsing lands
(Mengistu & Salami, 2007;
Tolera et al., 2012). However, the
contribution of free-ranging feed resources is subjected to the types
and species of livestock reared (Rahman et
al., 2008).
The current growing concern of scientists is mainly focused on
investigating how and to what extent climate and land use/cover changes
are affecting livestock feed resources bases in pastoral regions
(Nelson, 2012;
Oba, 2012). Forage availability, pastoral
herd mobility and herd composition are primarily affected by the current
dynamics of climate and land use/cover changes. Land use/cover and
climate change significantly vary across the agro-climatic condition,
topography/altitude, and types of main livelihood and production system
(Deressa, 2007;
Yesuph & Dagnew, 2019;
Žurovec & Vedeld, 2019). According to
Faurès et al. (2013), climate impact
mitigation and adaptation potentials depend on the agro-climatic
condition and land use/cover types. Therefore, possible interventions
could be provided depending on intensity and types of land use/cover and
specific to particular environments (Dale,
1997).
According to FAO (2015), livestock
diversity, both species and genetic, is a source of resilience and
facilitates adaptations in the face of environmental challenges. Species
and genetic diversity are potentially required to cope up with the
continual changes in climate, feed resources and newly emerging
diseases. The adaptive capacity of livestock species to climate extremes
and harsh environmental condition is likely to sustain the livelihoods
of the producers. The livelihoods of the poor pastoral community depend
on diverse livestock species, and hence the value of species diversity
remains essential in the face of environmental challenges.
Land use/cover change studies have extensively conducted concerning
drivers of the change and drought vulnerability
(Aklilu et al., 2014;
Meshesha, Tsunekawa, & Tsubo, 2012;
Reid et al., 2000). However, few studies
dealt with livestock feed availability with land use/cover change
(Aklilu et al., 2014) and none have dealt
with the availability of livestock feed resources with climate and land
use/cover changes in the southeastern pastoral region of Ethiopia.
The interconnection of climate and land use/cover change with household
perception on trends of livestock feed resources is less understood. The
current trends of climate and land use/cover changes and its potential
impacts on the availability of livestock feed resources, pastoral herd
composition, grazing land, the viability of livestock production and
livelihoods of pastoral communities have not yet investigated in a
southeastern pastoral region of Ethiopia. Furthermore, integrating land
use/cover and meteorological information with ground-based community
perception could provide a complete picture of the combined effects of
climate and land use/cover change on livestock feed availability and its
driving factors on herd composition. Therefore, this study presents the
combined effects of climate and land use/cover change on livestock feed
resource availabilities and livestock species composition in east Guji
zone of southeastern pastoral region of Ethiopia.
- MATERIAL AND METHODS
- Description of the study area
East Guji zone is one of the zones in Oromia regional state and is the
richest in livestock production in southeastern Ethiopia. NegelleBorana is the capital town of the Zone, which is located 610 km
to the southeast of Addis Ababa. Three pastoral and agro-pastoral
districts with the similar agro-ecological condition, namely Goro-Dola,
Liban and Gumi-Eldallo, were selected for this study. The selected
districts lie between 40 38’ 55” and
50 33’ 7” N latitude and
390 9’ 25” and 39058’ 37” E longitude. The study locations cover about
742,644.14 ha. The districts are located at a lower altitude (1370 and
1560 m.a.s.l). Thus, the climatic condition of the study area is a
mostly semi-arid condition with the bimodal rainy season. The average
annual rainfall is about 526.75 mm, and the temperature ranges from 24
to 30 ℃ (Adi & Swoboda-Reinhold, 2003;
Aklilu et al., 2014). The soil type of the
rangelands in the study areas are mainly vertisols
(Abate, 2016;
Dalle, Maass, & Isselstein, 2006).
- Data collection
- Land use/cover change analysis methods
- Data sources and methods of data collection
Land use/cover change analysis was conducted using time-series satellite
imageries downloaded from USGS web site (https:/glovis.usgs.gov/).
Landsat images of 1986, 1995, 2010 and 2018 with 167 path and 056/057
rows were used to generate land use/cover. As the study is large in
size, two adjacent Landsat images were downloaded for each period (Table
1).
Image analysis methods
The analysis was conducted in three different stages such as
pre-processing, image classification and post-processing. In the
pre-processing stage, the images were checked for their geometric and
radiometric errors. As the images downloaded from USGS are already
geo-referenced, there is no need to conduct geometric correction. With
regard to radiometric errors, all the images were subjected to haze
reduction and atmospheric correction. Once the images were checked for
their geometric and radiometric errors, the adjacent images were
mosaicked using ERDAS IMAGINE 14 software.
In the image classification stage, 24 sample-training areas were
collected from each land use/cover type and used for training as well as
accuracy assessment. Maximum likelihood classifier algorithm was used to
classify the images. Accordingly, six different land use/cover classes
such as agricultural land, bushland, forestland, woodland, grassland and
settlement were generated.
In the third stage, the classified images were checked for accuracy
using accuracy assessment technique. The training areas used for image
classification were used to assess the accuracy of the classified images
as well. Kappa coefficient was computed, and report about producer and
user accuracy was generated. In addition, a change matrix was computed
in ERDAS IMAGINE 14 software to determine the contribution of each land
use/cover class.
Estimation of potential dry-matter production of different
land-use type
Rangeland condition assessment was conducted at different years in the
same study area, and the condition varies over time. Potential
dry-matter supply of the rangeland during 1986, 1987-95 and 1996-2010
was estimated using Wroe (1988) based
model. The model considers the range of precipitation and rangeland
condition of the study area. Average annual precipitation of the study
area falls within the range of 550-650 mm at all study period (1986,
1987-1995 and 1996-2010). Accordingly, excellent and fair rangeland
condition was considered to determine the potential dry matter supply of
grassland in 1986 and 1987-1995, respectively. At the same time, the
average of good and excellent rangeland condition was used to estimate
total pasture supply of grassland during 1986. The grassland forage
supply of the study area for 2018 was estimated from the sample taken
from two different enclosed Kalo (reserve grazing). About 37 sample
plots were selected from two different sites the grass was cut at the
ground during the beginning of May (5-10) by throwing 1
m2 quadrant to measure the total dry matter supply in
the field. The clipped grasses were dried at room temperature over
several days, and then dried sample weight was weighed using digital
sensitive balance and recorded. Thus, the average value of dry-matter
productivity from the whole plots was used to estimate the total dry
matter supply of the grassland in the study area.
The total dry matter supply of bush or shrubland, woodland and dry
forest were determined using a simplified simulation model of
Timberlake and Reddy (1986). The model
considers potential evapotranspiration (PET), annual rainfall and soil
water retention capacity of the study area. Annual rainfall and
temperature data were obtained from the national meteorological agency,
Ethiopia. Potential evapotranspiration data was determined from the
monthly average maximum and minimum temperature using DrinC software
version 1.7 (91). About 233 mm of water retention capacity of the soil
was used, since vertisols at a depth of 0-1.5 m can have 233 mm of water
retention capacity in the lowland of Ethiopia
(Virmani, Sahrawat, & Burford, 1982).
About 75% of utilization potential was considered since the woodland,
shrub and dry forest flora of tropical Africa contain about 75% of
browse species that can be potentially utilized by goat and camel
(Wickens, 1980).
\(Rp=\ \overset{\overline{}}{R}-t_{50+p}*\left(\mu\right)\)…………………………………………
Equation 1
\({R^{\prime}}_{p}=\ \left(\frac{R_{p}}{\text{PET}}\right)*1800\)……………………………………………Equation
2
\(\text{AE}_{p}=\ {R^{\prime}}_{p}-\text{RO}_{p}\)………………………………………………Equation
3
\(\text{RO}_{p}=\ \left(\frac{15.2482}{K^{0.8}}\right)*\left(\frac{{R^{\prime}}_{p}}{100}\right)^{3}\)………………………………………….Equation
4