Chemical composition of experimental feeds and diets fed to cows during nylon bag degradability.
The aim of the chapter was to evaluate and predict the nutritive and feeding value of unknown and underutilised forages. Underutilised forages were collected from various regions. Chemical composition and degradability of forages in the rumen were determined. A dataset was created bearing degradability parameters of feeds from 40 studies. Using the dataset, a step-wise regression procedure was used to develop regression equations to predict rumen degradability. Of the underutilised forages, crude protein content tended to be double for Brassica oleracea var. acephala compared to Colophospermum mopane leaves and pods. Forage grasses tended to have very low crude protein contents compared to legumes and concentrates. Underutilised Brassica oleracea var. acephala tended to have higher crude protein levels compared to commonly used protein sources. The regression model for predicting the soluble fraction accounted for 59% (development) and 71% (validation) of the variation. The regression model for predicting the potential degradability accounted for 65% (development) and 24% (validation) of the variation. In conclusion, the nutritive value of underutilised forages was good, high in crude protein and high potential degradability. After correcting for factors that significantly affected degradability parameters, predicted solubility and effective degradability lay near the ideal prediction line, giving good predictions.
- Afzelia quanzensis
- Brassica oleracea var. acephala
- Colophospermum mopane
- feeding value
Ruminants such as cattle, goats and sheep are important livestock for resource-limited farmers around the world because of their ability to utilise readily available and cheap fibrous feeds that are otherwise not consumed by humans and monogastric livestock. Key to their ability to utilise feeds of high fibre content is the presence of fibrolytic bacteria in the rumen. There are a large number of plant species that have the potential of being used as forage for ruminants. Among them are a wide range of plants that are unknown to the public domain and some that are underutilised because of inadequate information on their feeding value. Exploration of these plant species is important in increasing the forage base for livestock farmers under gradually changing climatic conditions that are projected to reduce forage availability, quantity and quality. Determination of whether a forage crop can be a potential feed for a ruminant entails evaluation of its feeding value. Feeding value and quality of forages as feed for ruminants are evaluated through determining chemical composition, intake, palatability, acceptability and digestibility in vivo or in sacco. Degradability of feeds in sacco is one of the most widely used techniques to determine how much feed is digested in the rumen  and is important in determining feed intake. In developing countries, lack of rumen cannulated animals and/or nylon bags may hinder assessment of forage quality using rumen degradability of forages in sacco. There is a need for the development of simpler methods for the prediction of rumen degradation of forages. Simulation of digestibility of forages that has never been studied before is crucial for preliminary identification and selection of relatively unknown forages as a feed source for ruminants.
The broad objective of this chapter was to review, evaluate and predict the nutritive and feeding value of unknown and underutilised forages that have a potential of being ruminant feeds. The aim of this study was to: (1) evaluate rumen degradation of legume forages (
2. Review of relatively underutilised plants for feeding ruminants in sub-Saharan Africa
Non-conventional feeds and forages are feed resources used locally by farmers or have not been traditionally used in commercial or local feeding of livestock. These feeds can be available mostly with smallholder farmers and are used for short period of time, especially during the dry season when there is shortage of feeds. Literature has shown that non-conventional feeds (e.g. home waste) and forages (some forbs) are mainly used by smallholder farmers to cope during the dry season [2, 3, 4]. Although these non-convention forages are used occasionally, some of them have shown good quality attributes, which can sustain any ruminant livestock if they are provided a good quantity throughout the year. For example, bitter leaf (Vernonia), corn plant, snake weed and commelina  have an acceptable metabolisable energy (ME) of >7 MJ/kg DM, which is comparable to well-known Lucerne hay (7.8 MJ/kg DM; ). Browse plants include
The improvement of these feed resources could increase its availability year-round and reduce the length of the critical period when feed is in short supply. However, a cursory review of literature has depicted a paucity of information on efforts to improve and promote new options related to these feeds. Notwithstanding little is known about non-conventional feeds, it is not easy to encapsulate technological challenges on these feeds. Nonetheless, anecdotal information shows that technological challenges to include these feeds are related to (1) less interest on these feeds; many plant breeders are much more interested in food crops than forages, leading to poor testing and selection of the best-bet forages among the latter based on their agronomic aspects, (2) lack of information on these feeds at local prevailing conditions and on their potentiality (biomass production and nutrient value). Some of these underutilised forages are described below.
Mopane trees are widely distributed in the hot arid steppe areas of Southern Africa and are mainly concentrated between Southwestern Zimbabwe and Northeastern Botswana. Mopane shrubs grow in hot, dry, low-lying areas with alkaline soils. During periods of feed scarcity, cattle, goats and sheep tend to browse on Mopani tree leaves and pods. Goats prefer to browse on Mopane leaves and pods when they are reddish-brown in colour probably coinciding with high pH > 5 and low levels of condensed tannins.
Brassica oleracea var. acephala
Commonly known as African kale, Chou Moellier and/or chomollier, this plant species thrives in well-drained soils with good soil quality and may be grown after turning in a green manure such as vetch or clover. Predominantly grown as a vegetable crop for human consumption [19, 20], little is known of the nutritional value of Chou Moellier leaves as a supplement feed source for ruminants, especially goats and sheep. There are claims that dairy cattle farmers in some parts of Australia and New Zealand use
Although the cassava root remains a good source of food for humans, cassava peels and chips may be fed to ruminants as household waste to provide supplementary nutrients. Tested in cattle, the a-fraction and effective degradability of dry matter, organic matter and crude protein were highest for cassava chips compared to generally preferred energy concentrates namely, ground corn, broken rice, rice bran and rice pollard . Supplementation of rice straw with sun-dried cassava (at 1% body weight) foliage increased dry matter intake (+1341 g/d), crude protein intake (+239 g/d) and average daily gain (+201 g/d) compared to unsupplemented rice straw fed heifers . In addition, molar proportions of propionic acid were higher in cattle supplemented with cassava at 2 and 3% body weight, leading to significantly low acetate: propionate ratio in the rumen . Fermentation shifts towards propionic acid production are implicated in reduction in methane emissions from the rumen. The response of microbial nitrogen supply to increased levels of supplementation of cassava was a positive quadratic peaking (186.6 ± 0.85 gN/d) at 2% BW supplementation. Wanapat and Khampa  recommended the use of a cost-effective option to supplement using cassava at inclusion rates of 2% body weight by smallholder beef and dairy farmers. Cassava may thus play a critical role in improving the nutritional status of ruminants in tropical and sub-tropical areas coupled by its environmentally friendly role of reducing methane emissions.
Sclerocarya birreassp. caffra
The Marula tree fruit is a common feed supplement for ruminants in parts of Northwestern Nigeria , but generally not fully exploited in most parts of Southern Africa, given its abundance in the region. Full exploitation of Marula oil cake (MOC) as a supplement in ruminant diets may be limited by the scarcity of its feeding value for ruminants. Crude protein content of MOC is about 324–472 g/kg [30, 31] and may be comparable with those of commonly used protein supplements, soya bean meal (SBM) and sunflower cake (SFC) . Several studies have evaluated the potential benefits of MOC as a supplement for ruminants with positive results; substitution of urea with MOC as a source of nitrogen in fattening rations had no undesirable effects on dry matter feed intake (fattening ration plus urea = 6.38 vs. fattening ration plus MOC = 6.84 kg/day) and growth rate (fattening ration plus urea = 1.62 kg/d vs. fattening ration plus MOC = 1.75 kg/d) of feedlot cattle, while a combination of equal amounts of urea and MOC in the fattening ration tended to maintain similar intakes (7.07 kg/day), but yielding better growth rates (1.82 kg/d) in feedlot cattle . Potential degradability (PD) of MOC in the rumen was 723–857 g/kg for dry matter, while the PD of crude protein was 844–963 g/kg  in goats. Nitrogen retention was higher in goats that fed grass hay supplemented with MOC (2.8 g/d) compared to SBM (1.1 g/d) and SFC (−0.6 g/d) . This suggests that
With appreciable amounts of crude protein of 180–255 g/kg , pre-suckling kids grazing and supplemented with
Commonly known as Monkey orange,
3. Nutritive value of some underutilised forage crops
3.1. Evaluation of the nutritional value of underutilised forages and roughages
3.1.1. Materials and methods
Underutilised forage legumes and forage trees and shrubs (non-leguminous) were collected from various regions. These forages included
Eleven commonly used forages (10 forage grasses and 1 legume forage) were collected in KwaZulu-Natal, South Africa. These roughages included cowpea leaves and stems (
Moisture, dry matter (Method 934.01), organic matter and ash content (Method 942.05) of these forages and roughages were analysed using the procedures described by the Association of Official Analytical Chemists . Nitrogen content was determined using the LECO TruSpec nitrogen analyser (LECO FP2000, LECO, Pretoria, South Africa). Crude protein content was calculated by multiplying the nitrogen content by a factor of 6.25 (crude protein = nitrogen content × 6.25). Neutral detergent fibre, acid detergent fibre and acid detergent lignin were analysed using ANKOM A220 fibre analyser (ANKOM Technology, New York, USA). Hemicellulose content was calculated as the difference between neutral detergent fibre and acid detergent fibre content (hemicellulose = neutral detergent fibre—acid detergent fibre). The cellulose and acid detergent lignin content were determined using the method of Van Soest and Wine .
The nylon bag technique  was used to determine the degradability of forages and roughages in the rumen. Dried forages were milled to pass through a 2-mm screen using a hammer mill (Scientec hammer mill 400, Lab World Pty Ltd., Johannesburg, South Africa). Approximately 4 g of each ground forage sample was weighed into ANKOM nylon bags (ANKOM Co, Fairport, New York, USA; internal dimensions: 5 × 9 cm; pore size 50 μm) and sequentially incubated (in triplicates per time interval) in the rumen for 120, 96, 72, 48, 24, 9, 6, and 3 hours using four non-lactating Jersey cows (body weight = 330 ± 19.97). The cows were fed on veld hay (
3.1.2. Mathematical procedures
Degradability of forages was determined using dry matter loss (DML) in nylon bags. A curve for DML against incubation time was plotted and used to inspect for outliers. The model of McDonald  was fitted on Statistical Analysis System 9.3 (SAS Institute Inc., Cary, NC, USA) to generate degradation parameters of the forages. The model used was as follows: Y = a + b(1–e–c(t–L)), where Y is the degradability at time (t), a is the intercept, b is the potentially degradable fraction, c is the rate of degradation of b and L is the lag time. Effective degradability (ED) was calculated using a predicted passage rates for each forage. The passage rate of solid was predicted using models developed by Moyo et al. .
Of the underutilised forages, the crude protein content tended to be double as much for
There was not much of a difference between the potential degradability of forage grasses (651 ± 111 g/kgDM), concentrates (756 ± 95.4 g/kgDM), and forage legumes, trees and shrubs (745 ± 110.2 g/kgDM) (Tables 3–5).
DM: dry matter, OM: organic matter, N: nitrogen, NDF: neutral detergent fibre, ADF: acid detergent, ADL: acid detergent lignin, HEM: hemicellulose, CEL: cellulose, VGH: veld grass hay, LH: lucerne hay.
MS: maize stover, ML: maize leaves, MT: maize stalks, WS: wheat straw, EC:
MS: maize stover, ML: maize leaves, MT: maize stalks, WS: wheat straw, EC:
4. Is it possible to predict the rumen digestibility (feeding value) of unknown and underutilised forages?
4.1. Prediction of degradation of forages in the rumen using feed and animal properties
4.1.1. Materials and methods
Data were collected from studies that reported at least average values for in sacco (nylon bag technique) degradability parameters (a, soluble fraction; b, slowly degradable fraction and c, rate of degradation) of roughages and stated the diet, feeds and feed supplements given to animals. A dataset was created bearing degradability parameters from wild and domesticated ruminants from 40 studies. Factors affecting degradability were identified in each of these studies and were categorised into two main groups: (1) diet properties (i.e. fed to the animal) and (2) feed sample properties (i.e. incubated in the rumen). Diet properties were used to account for the effects of rumen ecology on fermentation and included neutral detergent fibre (NDF), starch (STA) and crude protein (CP) contents of entire diet (all in g/kg), level of concentrate supplementation (%) and provision of a urea supplement in the form of a lick (presence = 1, absence = 0). Feed sample properties included urea treatment (%) of sample and feed compositional attributes (DM, dry matter; CP, crude protein; NDF, neutral detergent fibre, ADF, acid detergent fibre; HEM, hemicellulose and ash all in g/kg). Starch content of the diet fed to animals was calculated using the formula: STA = 1000–(NDF + CP). Potential degradability (PD) and hemicellulose (HEM) content were calculated in studies that did not report them using the formulae: PD = a + b; and HEM = NDF—ADF, respectively. Studies that did not report dietary composition of feeds but mentioned names of feeds used had their composition looked up in studies that reported them. These factors were used as input parameters to develop regression models for predicting degradability of feeds in the rumen.
A step-wise regression procedure on the Statistical Analysis System 9.3 (SAS Institute Inc., Cary, NC, USA) was used to select parameters that qualified to develop regression equations to predict (1) rapidly degradable fraction of fibre (a), (2) potential degradability (PD), (3) time lag for fermentation to occur (tL), and (4) rate of degradation (c) in the rumen. One parameter from a pair of correlated parameters was dropped in model development when both correlated parameters significantly influence degradation parameters. Those parameters that qualified for model development were CP and NDF content of feed sample (model for soluble fraction of fibre); ADF content of feed sample and STA content of diet (model for potential degradability); ADF, CP and ash content of feed sample, and STA content of diet (model for time-lag); NDF and CP content of feed sample, and, STA and DNDF content of diet (model for degradation rate).
Regression models were used to simulate the rumen degradability of
4.1.2. Statistical analyses
For all evaluations, regression analyses of observed against predicted degradability were carried out using the linear regression procedure. Coefficients of determination (R2) were used to evaluate the precision of regression lines in approximating real data points of models and standard error of the mean (SEM) was used to determine the accuracy of prediction.
4.2.1. Model development
From the step-wise regression procedure for all prediction models, level of concentrate supplementation, provision of a urea supplement in the form of a lick and urea treatment of feed sample were rejected in model development.
The regression model for predicting the soluble fraction (a) was a = 558.12(±62.45) + 0.27(±0.133) CP–0.57(±0.07) NDF (n = 113, SEM = 6.86), accounting for 59% of the variation in development.
The regression model for predicting the potential degradability (PD) was PD = 1025.96(±66.64)–0.91(±0.10) ADF + 0.32(±0.08) STA (n = 113, SEM = 9.27), accounting for 65% of the variation in development.
The regression model for predicting the time-lag (tL) was tL = −11.33(±1.89) + 0.030(±0.002) ADF + 0.01(±0.003) CP–0.006(±0.001) STA + 0.02(±0.007) ASH (n = 113, SEM = 0.17), accounting for 77% of the variation in development.
The regression model for predicting the rate of degradation (c) was c = 0.12(±0.05) + 0.00013(±0.00002) CP–0.00012(±0.00006) STA–0.00002(±0.00001) NDF–0.00008(±0.00005) DNDF (n = 113, SEM = 0.0009), accounting for 55% of the variation in development.
4.2.2. Model predictions
The regression model for predicting the soluble fraction of feeds accounted for 70% of the variation in prediction for forage legumes, trees and shrubs, forage grasses and concentrates (Figure 1).
The regression model for predicting the potential degradability accounted for 24% of the variation in prediction for forage legumes, trees and shrubs, forage grasses and concentrates (Figure 2).
The regression model for predicting the slowly degradable fraction of feeds for forage legumes, trees and shrubs, forage grasses and concentrates (Figure 3).
The regression model for predicting the rate of degradation accounted for 4% of the variation in prediction for forage legumes, trees and shrubs, forage grasses and concentrates (Figure 4).
The regression model for predicting the effective degradability of feeds accounted for 57% of the variation in prediction for forage legumes, trees and shrubs, forage grasses and concentrates (Figure 5).
Among the forage legumes, trees and shrubs,
Compared to concentrates used in the study,
Relationships between two variables are said to be ideal when the coefficient of determination (R2) is in unity; any deviation from the unity degree indicates the degree of imperfection. The above parameters were used to determine the effective degradability (ED): (ED = a + (PD−a) × c/(c + kp); where ‘a’ is a soluble fraction, PD is the potential degradability, ‘c’ is the rate of degradation and kp is the rate of passage of particles through the rumen. Effective degradability is equivalent to digestibility in the rumen. The predicted effective degradability indicated in Figure 5 followed the expected trends, suggesting that these models (for predicting ‘a’, PD, and ‘c’) in the meantime can be used for this purpose. The overall trend between the observed and the predicted digestibility is positive, though accounting for just 36–52% of the total variation , which does not compare favourably with R2 of 70% obtained with the application of the simulation model to temperate roughages  and those from this study. The amount of variation accounted for in observed against predicted digestibility for simulations by Nsahlai and Apaloo [49, 50] was comparably higher than those reported in empirical studies by Shem et al. , Kibon and Orskov  and Umunna et al. .
The rather low precision in predicting the rate of degradation (mainly for concentrates, legume forages, trees and shrubs) and the potential degradability (concentrates) of feeds in this study may have been due to the fact that the studies that were used in model development reported data on degradation of roughages grasses only, which are generally of low quality, and did not use data on concentrates, legume forages, trees and shrubs. Despite this, simulations of solubility and effective degradability were good, suggesting that slight modification of model parameters may give better prediction of all degradability (nutritive value) of a large number and classes of forage crops. Generally, there is a poor simulation of digestibility for low quality roughages, which are commonly grazed and fed to ruminants in the tropics. Ambient temperature grossly affects the digestibility of plant material through its influence on lignin deposition in plants. Studies should focus on development of digestibility models that account for variability in diet quality as brought about by ambient temperature. Future studies may need to account for the type of model used in computation of degradation parameters.
The nutritive value of underutilised forages,
This study was financially supported by the National Research Foundation (NRF) of the Republic of South Africa (project name: Modelling of intake, feeding behaviour and kinetics of digestion and passage of digesta in ruminants; grant unique number: 112905) and the University of KwaZulu-Natal (competitive grant number: P029).
Conflict of interest and declaration
The authors declare that they have no competing interests. We affirm that all the authors of this manuscript agree to the submission, and the manuscript has not been submitted to be published in or considered for publication anywhere else. The views expressed in the paper are those of the authors and not of the National Research Foundation (NRF) of the Republic of South Africa.