Effects of grazing intensity on forage nutritive value of dominant grass species in Borana rangelands of Southern Ethiopia

Site selection

Both site selection and data collection are done following the same procedures used previously by Fenetahun et al. (2020) in the study “Dynamics of forage and land cover changes in Teltele district of Borana rangelands, southern Ethiopia: using geospatial and field survey data”. The study is conducted in the Teltele semi-arid rangeland in the Borana zone of Southern Ethiopia, by selecting areas that are no-grazed (NG), moderately grazed (MG), and overgrazed (OG) as a treatment using the calculated carrying capacity of those areas for two consecutive seasons in 2019 (Fig. 1) (Fenetahun et al., 2020). The study site is located between 04°56′23″N latitude and 37°41′51″E longitude (Fenetahun et al., 2020; Dalle, Maass & Isselstein, 2015), and it is selected because it is one of the most arid parts of Borana zone and, therefore, the pastoral communities of this region are the most vulnerable to the rangeland degradation as a result of overgrazing (Fenetahun et al., 2020). The area is located 666 km south of Addis Ababa, the capital city of Ethiopia (Fenetahun et al., 2020). The elevation is about 496–1,500 m with a maximum elevation of 2,059 m above sea level (Fenetahun et al., 2020). Rainfall is bimodal with the main (60%) rainy season occurring between March and May, while the short (27%) rainy season occurs from September to November (Dalle, Maass & Isselstein, 2015; Fenetahun et al., 2020) (Fig. 2). The two intervening dry seasons are from June to August and December to February, when forage resources are scarce (Fenetahun et al., 2021; Angassa, 2014). The mean annual rainfall recorded over the past 12 years (2008–2019) is 450–700 mm (NMA, 2019, and Gemedo, 2020), while the mean annual temperature varies from 28 to 33 °C with little seasonal variation (Fenetahun et al., 2020). The annual potential evapotranspiration of the area is 700–3,000 mm (Billi, Alemu & Ciampalini, 2015). The main soil type in the study area, composed of 53% sandy, 30% clay, and 17% silt, is mainly used to support the growth of grass species for grazing (Fenetahun et al., 2020; Fenetahun et al., 2021; Coppock, 1994; Gemedo, 2020). The rangeland vegetation is mainly dominated by encroaching woody species and those that are frequently thinned out, including Senegalia mellifera, Vachellia reficiens, and Vachellia oerfota (Coppock, 1994; Gemedo, Maass & Isselstein, 2005; Fenetahun et al., 2020). The 2015 national census reported a total population of 70,501 for this woreda, including 36,246 men and 34,255 women, and 4,874 (6.91%) urban dwellers. At that time, cattle, goats, sheep, camel, mule, donkey, and horse were the main species of livestock species reared in the area (Fenetahun et al., 2020). Furthermore, according to data reported by the zone livestock office, the estimated total number of herds for all species is 201,148, and the proportions of each species found in the study district are: cattle 92,000 (45.7%), goats 58,139 (28.9%), sheep 17,210 (8.6%), camels 15,305 (7.6%), horses 8,000 (4.9%), mules 3,494 (1.7%), and donkeys 7,000 (3.5%). For the OG site, all species graze year-round without rest, as pastoralist migration from one area to another is highly limited by government policies, which is a major cause of overgrazing and greatly impacted the nutritive value of grass species in the Borana rangelands.

Experimental design and sample collection

Data is collected using the same procedures described by Fenetahun et al. (2020), as the same site is studied. We select the site using three treatments: a no-grazed (NG) site (as a control), a moderately grazed site and an overgrazed site (used to examine the effect of grazing intensity) based on grazing intensity gradient (Fenetahun et al., 2021) and the current carrying capacity potential (Fenetahun et al., 2020; Fenetahun et al., 2021). Inside the NG site, livestock has been abandoned, so the site is used as a reference to compare the forage nutritive value with other areas and to evaluate the impact of GI. The livestock species found in all grazing treatments are the same and the only variables are density and GI. Once the forage yield and utilization rate are determined, the carrying capacity (CC) can be calculated. The information can be used in two alternative ways: (a) to determine stocking rate or the number of heads of animals a system can carry in terms of the total livestock unit (TLU ha⁻¹ year⁻¹), or (b) to determine how much area a specific herd can graze in the system (ha TLU⁻¹ year⁻¹) (FAO, 1988). Similar to the CC calculation, 30% consumable rate is applied on the potential yield to calculate the stocking rate using the following formula: (1)${\displaystyle \mathrm{Stocking rate for the year}\left({\mathrm{TLUha}}^{-1}{\mathrm{year}}^{-1}\right)=\mathrm{TLU}/\mathrm{total grazing area}}$

The treatment sites for sample collection involves a stocking rate of NG (∼0 TLU ha⁻¹Y⁻¹), MG (2 TLU ha⁻¹ Y⁻¹), and OG (4 TLUha⁻¹ Y⁻¹ and above) based on the current forage biomass yield and carrying capacity of rangeland calculated by applying the approach proposed by Fenetahun et al. (2020) and through physical field observation. The treatments with different GI are selected and investigated at 2 km intervals (Fenetahun et al., 2021). The selected rangeland sampling areas of these three GI sites are 100 ha for each (in total one NG + one MG + one OG sites = 300 ha) (Fenetahun et al., 2021). The sites are selected from a homogeneous area and have similar geographical conditions like slope, elevation, and soil types (Fenetahun et al., 2021). The grazing treatments and sample collection are implemented both during the dry season (from December 2018 to February 2019) and the rainy season (from March to May 2019) at the time where grass species can be easily observed and peak biomass is recorded to evaluate the interactional effect of seasonal variation combined with GI, with three replications for each season (EMA, 2019; Fenetahun et al., 2021). Then, after establishing a 5 km transect both in the NG and grazing sites, five 50 m × 50 m plots at 500 m intervals are established, resulting in a total of 15 plots (three treatments with five plots each). Then, in each plot, three 5 m × 5 m subplots are randomly assigned as pseudo-replicates out of the total of 45 subplots in grazing and non-grazing treatments (Fenetahun et al., 2020; Fenetahun et al., 2021). Finally, five 1 m × 1 m quadrats in each subplot, with a total of 225 quadrats per season (450 quadrats across the two sampling seasons) are assigned by randomly casting them back side to minimize any bias resulting from selective placement in each subplot for the collection of samples of grass species over two consecutive seasons (Fig. 1) (Fenetahun et al., 2020; Fenetahun et al., 2021). The total sampling of 5 plots × 3 sub-plots × 5 quadrats × 2 seasons × 3 replications = 450 for each treatment site is conducted (Fenetahun et al., 2020; Fenetahun et al., 2021). Moreover, the same sample collection techniques and treatment sites are adopted during the dry and rainy seasons (Fenetahun et al., 2020; Fenetahun et al., 2021). In each sampling unit, we record the dominant grass species and abundance for each grass species (Fenetahun et al., 2020; Fenetahun et al., 2021). All the ground grass samples are obtained by using a cutter and each grass species is stored separately in a paper bag (Fenetahun et al., 2020; Fenetahun et al., 2021). The fresh weight of the collected grass samples is measured in the field with a scale (Fenetahun et al., 2020; Fenetahun et al., 2021). Then, the samples are oven-dried for 48 h at 55 °C to value the biomass. The sub samples are used to calculate the dry weight of forage mass and estimate forage nutritive value as described below (Fenetahun et al., 2020; Fenetahun et al., 2021). The dried samples are first measured and grounded to pass a one mm screen for further analysis at a lab of College of Agriculture and Environmental Sciences, Bahir Dar University, Ethiopia. The evaluated forage comprising five dominant grass species (Chloris roxburghiana, Cenchrus ciliaris, Chrysopogon aucheri, Aristida kenyensis and Digitaria milanjiana) is selected and sampled based on relative abundances (≥40%) and pastoralists’ experiences and preferences for certain grass species (Habtamu et al., 2013). Grass species are identified in the field by using identification keys, plates, the book Flora of Ethiopia, and the National Herbarium of Addis Ababa University (Dalle, Maass & Isselstein, 2015). The specific assessment for detailed acceptability values of dominant grass species and soil physicochemical properties is given by a study carried out on the same site and by the same author (Yeneayehu, Xu & Wang, 2020).

Forage nutritive value analysis

Forage samples are analyzed for multiple quality factors on a dry mass basis, with a crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), ash, dry matter digestibility (DMD), potential dry matter intake (DMI), and relative feed/forage value (RFV) following standard procedures as described below (Table 1). The calculated results of CP, NDF, ADF, and ADL are presented using g/kg as unit and ash, DMD, RFV and DMI are expressed in the form of percentage (%). Based on the obtained results of NDF, ADF, DMD and DMI, the forage nutritive quality of the grass species can be estimated and ranked by adopting the following formula (Evitayani et al., 2004; Fazel, Abdul & Mohamed, 2012). DMI, calculated as a percentage, estimates the relative amount of forage an animal will eat when only forage is fed (Undersander, Mertens & Thiex, 1993).

(2)${\displaystyle \text{\%}\mathrm{DMI}=120/\text{\%}\mathrm{NDF}}$

(3)${\displaystyle \mathrm{RFV}=\frac{\mathrm{DMD}\left(\text{\%}\right)\times \mathrm{DMI}\left(\text{\%}\right)}{1.29}}$

RFV = Relative forage value of forage species predicted by NDF and ADF.

Statistical analysis

Forage nutritive value data is analyzed using SPSS Version 22 with grazing treatment and season as well as their interactions as fixed factors, and plot considered as a random factor. Plot is treated as a repeated measure. There are 450 sample observations (5 plots × 3 sub-plots × 5 quadrats × 2 seasons × 3 replications) in each GI site for each forage variable (CP, ADF, ADL, DMD, DMI, NDF). Analysis for repeated measures concerning forage nutritive values is performed using a mixed model (Proc Mixed), including GI (NG, MG and OG), season (dry and wet), and their interactions as repeated effects. A two-way analysis of variance (ANOVAs) followed by a Duncan’s multiple range test is performed to test significant differences (P < 0.05) between NG, MG, and OG treatments within and between each season. A simple linear regression analysis is conducted to examine the relationship between GI and various variables (from forage nutritive value response ratio to grazing in each season). A principal component analysis (PCA) is carried out to examine the relationship between forage nutritive values of the species based on the experimental results.

Effects of grazing intensity on forage nutritive value

The relative abundances of the selected dominant grass species across all grazing intensities and seasons are presented in Fig. 3. Based on the recorded data, C. roxburghiana, C. ciliaris and C. aucheri are >60%, A. kenyensis is ≥50%, and D. milanjiana is ≥40% in abundance both during the rainy and dry seasons. C. aucheri is the most abundant grass species in Teltele rangeland, followed by C. ciliaris and C. roxburghiana. The effects of GI on forage nutritive value varies significantly (P < 0.05) in terms of CP, ADF, NDF, ADL, ash, and RFV contents both within and between each species (Table 2). For all grass species, the concentrations of CP and ash increase when GI rises, while the concentrations of ADF, NDF, and ADL decrease when GI drops. For the dominant grass species, the effect of GI on the forage nutritive value is described using linear regression analysis (Fig. 4).

According to the regression analysis results, the RFV of forage shows significant differences (P > 0.05) under different GIs. It presents a decreasing pattern if the concentrations of ADF, NDF, and ADL value are increasing, yet shows an increasing pattern when the concentrations of CP and ash value increase (Fig. 4). The RFV value could be used to estimate the forage quality. The higher the RFV value is, the higher the quality is. C. roxburghiana, A. kenyensis, and C. aucheri are grass species with high RFV (forage quality) based on our research data.

In the NG site, the concentration of CP for all grass species is below the minimum requirements for beef cattle (7%) with the exception of C. ciliar (7.7%) and for small ruminants (9%) (Gemedo, 2020; Habtamu et al., 2013). Across all GIs, the concentration of CP for C. roxburghiana, A. kenyensis and D. milanjiana is below the minimum requirements for most grazing animals. This is because the grass maturity at the NG site increases and results in a lower leaf-to-stem ratio, leading to an increase in ADF, NDF and ADL, and a decrease in CP. At the NG site, the forage quality decreases as compared with both the MG and OG, due to decreasing digestibility and increasingly maturing proteins.

On the other hand, with an increasing GI, the rate of new growth declines, and the consumption of less desirable materials, such as the remaining part of mature forages from the previous growing season, results in a decreasing CP concentration. The concentrations of ADF, NDF, and ADL are highest for D. milanjiana, followed by C. ciliary, and lowest for C. roxburghiana, followed by A. kenyensis. The concentration shows a decreasing pattern when it switches from NG to OG and presents a significant difference (P < 0.05) both within and between species and across GIs. High ash content is recorded for C. ciliary and low for C. roxburghiana, showing a significant variation (P < 0.05) both within and between species and across GIs, and increases at the OG site followed by the MG and the NG site respectively.

The interaction effects of grazing and season on forage nutritive value

The nutritional composition of the forage grasses is significantly different (p < 0.05) among and within species due to the interactive effects of season and GI rate (Table 3). Therefore, the grazing season affects not only the biomass production of rangelands but also the nutritive value of existing forage grass species in the grazing site. The result indicates that the concentrations of CP, ash, DMD, and DMI content increase during the rainy season compared with those in the dry season. Across all grass species, the highest values of CP, ash, DMD, and DMI content are recorded at the OG site during the rainy season, whereas the lowest values are recorded at the NG site during the dry season.

The CP content varies from 1.3% (A. kenyensis) to 11.4% (C. ciliari) during the dry season and from 6.9% (A. kenyensis) to 18.9% (C. ciliari) during the rainy season. The ash content varies from 8.1% (C. roxburghiana) to 14.8% (C. ciliari) during the dry season and from 13.5% (C. aucheri) to 21.9% (D. milanjiana) during the rainy season, indicating that the concentrations of CP and ash are higher during the rainy season than in the dry season. The fiber constituents (i.e., ADF, NDF, and ADL) of the forage grass species increase during the dry season compared with the values in the rainy season. The highest values of ADF, NDF, and ADL are recorded at the NG site during the dry season, whereas the lowest values are recorded at the OG site during the rainy season (Table 3). ADF, NDF and ADL vary from 29.5% (C. roxburghiana) to 47.1% (D. milanjiana), from 37.1% (A. kenyensis) to 60% (D. milanjiana), and from 13.1% (A. kenyensis) to 33.7% (D. milanjiana) during the rainy season respectively, and increase in the dry season: from 38.7% (C. roxburghiana) to 59.9% (D. milanjiana), from 59.3% (C. roxburghiana) to 88.5% (C. ciliary), and from 26.6% (A. kenyensis) to 53.1% (C. ciliary) respectively. The interaction effect also impacts the RFV of forage and increases during the rainy season compared with that in the dry season. The maximum RFV is observed during the rainy season across all GIs, highest in the OG site while followed by the MG and NG sites, and lowest during the dry season in the NG site, followed by the MG and OG sites. A. kenyensis, C. aucheri and C. roxburghiana show the best, the second best and the third best forage qualities respectively based on our research results.

Based on the RFV data, the highest ranking grass species has high CP and ash contents and low fiber components, indicating that high forage quality is generally related to high CP and low fiber contents. Based on this, RFV is assumed to have a direct relationship with CP and ash contents and an inverse relationship with fiber components in forages. All grass species show significantly (P < 0.05) higher CP, DMD, DMI, and RFV contents and lower ADF, NDF, and ADL contents in the rainy season than in the dry season. The seasonal variation is caused by maturity and age difference of forage grass species and results in variation in nutritional compositions of forage grass species within the same grazing site.

Evaluating the proportions of nutritional contents in grass using forage quality index

Using Principal Component Analysis (PCA), the relationships between nutritional contents and various affecting factors are evaluated. The correlation matrixes of forage nutritional contents related to the impact of the seasonal variation in combination with GI and GI independently are analyzed and illustrated (Tables 4 and 5) respectively. The plotted eigenvalues are obtained from the correlation matrixes and variation also calculated and explained by the components (Fig. 5).

In Tables 4 and 5, there is a strong negative correlation between CP and ash contents, and fibers (ADF, NDF, and ADL) are observed in all grass species given different grazing season, GI rate, and their interaction effect. RFV shows a strong negative correlation with ADF during the dry season and at the NG site. Component loadings with varimax rotation, as well as the eigenvalues show that there are only two components with eigenvalues higher than one (Fig. 5A) and the total variance is 87.067% (Table 6). Component 1 contains 60.564% of the total variance of forage nutrient contents (CP, ash, DMD, DMI, and RFV) and Component 2 contains 25.503% of the total variance of forage nutrient contents (ADL, NDF, and ADL) (Fig. 5B). On the one hand, a positive correlation exists between CP, DMD, DMI and RFV, as well as between ADF, ADF and ADL of the forage nutrient contents. On the other hand, a negative correlation between fiber contents (ADF, NDF, and ADL) and CP, ash, DMD, DMI, and RFV is observed (Fig. 5B).