**4.3 On optimal channel distribution (future study)**

Fig. 13 shows two arrangements each of 18 radio channels for use by 4 call streams. Fig. 13.a shows a two-tier network with 3 individual channels per stream in the first tier and 6 common 12 Will-be-set-by-IN-TECH

b) Static reservation: Divide all macro-cell channels allocated to a cell into two groups: one for the common use by all calls and the other for high-mobility calls only (the rigid

(a) (b)

networks: (a) Three micro-cells and one macro-cell, (b) Seven micro-cells and one macro-cell. Numerical results for two two-tier network examples are obtained. They are not qualitatively different from the results of the one-tier model discussed above. Fig. 10.a (three micro-cells and one macro-cell) and Fig. 10.b (seven micro-cells and one macro-cell) show that the dynamic reservation strategy gives the higher maximum revenue in both cases if the reserved number of channels *R* is properly chosen. The parameters are as follows: *A* = high-mobility call flow, *B* = low-mobility call flow, *N*1 = number of micro-cell channels for each cell, *N*2 =

**Conclusion:** In case of two-tier network, the results of numerical analysis confirm that the

In hierarchical overlaying cellular networks, traffic overflow between the overlaying tiers is used to increase the utilization of the available capacity. The arrival process of overflow traffic has been verified to be correlated and bursty. This characteristic has brought great challenges to performance evaluation of hierarchical networks. In most published works, the discussion is focussed on traffic loss analysis in homogenous hierarchical networks, e.g. micro/macro cellular phone systems as in the numerical analysis below. In the paper (Huang et al., 2008), the authors address the problems of performance evaluation in more complicated scenarios by taking account of heterogeneity and user mobility in hierarchical networks. They present an approximate analytical loss model. The loss performance obtained by our approximated analytical model is validated by simulation in a heterogeneous multi-tier overlaying system. Fig. 12 shows the dependence of revenue on channel rearrangement from macro-cell to micro-cell. We are looking for maximum revenue when low-mobility calls cost one unit and

Fig. 13 shows two arrangements each of 18 radio channels for use by 4 call streams. Fig. 13.a shows a two-tier network with 3 individual channels per stream in the first tier and 6 common

Fig. 10. Dependence of the revenue on the reserved number of channels for two-tier

division-based CAC scheme).

number of micro-cell channels.

**4.2 Channel rearrangement effect**

high-mobility calls cost *K* = 3 units.

**4.3 On optimal channel distribution (future study)**

optimal strategy is dynamic reservation.

Fig. 11. An illustration of channel rearrangement.

Fig. 12. Dependence of revenue on channel rearrangement from macro-cell to micro-cell.

channels in the second tier. In Fig. 13.b some kind of a homogeneous single tier network is depicted: each call has access to 9 channels equally distributed between streams. Such kind of arrangement could be implemented by modern DSP techniques.

Fig. 14 depicts the loss probability curves for these two schemes. Case (a) relates to pure loss system, case (b) relates to scheme with one waiting positions per stream. What is surprising? In case (a), beginning with a loss probability as low as 0.56% (less than 1%), it is advantageous to use the equally distributed scheme. Therefore, the traditional two-tier network could be recommended here at a very low call rates. Table 1 contains more detailed data on loss probability. When a single waiting position is added, the advantage of the equally distributed scheme increases even more and the cross point of curves occurs at the loss probability equal to 0.025%.

.......................................................................................................................................................................................... ........

V

*M* = *L* · *E*(*N*, *L*) (10)

M1 ....................................................................................................................................................... ....... .......................... (11)

(13)

(14)

≥ 1 (12)

*L N* + 1 − *L* + *M*

*L N* + 1 − *L* + *M*

M

.......................... ................................................................................................................... ........ .......................... L N ∞

**5.3 Kosten's model and its new interpretation**

(reference is usually made to Riordan's paper (Riordan, 1956)):

*V* = *M* ·

*Z* = *V M* =

Fig. 17. An illustration of the ERT-method extension.

After substitution of (*M* + *N* + 1) into (11) we get:

1 − *M* +

 1 *M* − *M*1

*V* = *M* ·

We can reduce this expression to a simpler form:

*V* = *M*2

formula (7) follows:

or

probability a traffic stream with given mean value and variance is subject to.

.......................... ................................................................................................................... ....... .......................... L N 1

1 − *M* +

From these two parameters one introduce a new parameter *Z*, the so-called peakedness:

Experience shows that peakedness *Z* is a very good measure for the relative blocking

We offer a new interpretation of Kosten's results. We consider the scheme in Fig. 17. There are *N* common channels and one separate channel. From (10 and (11 we get a new formula for the variance *V* when both mean *M* and mean *M*1 = *L* · *E*(*N* + 1, *L*) are known. From recurrence

.......................................................................................................................................................................................... .......

*M*1 = *L M*

*L L M M*1 − *L*

> − 1 = *M*

Note that *M* − *M*1 is the load carried by a single channel and therefore it is always less than one. Formula (14) is useful for applications of the ERT-method in case of traffic splitting.

*M* + *N* + 1 = *L M*

= *M*

*N* + 1 + *M*

*M*1

1 − *M* +

 *M M* − *M*1

*M*1 *M* − *M*1

− *M*  M V

1 − *M* +

Fig. 16. Kosten's model: *N* fully accessible channels and a channel group of infinite capacity.

Call Admission Control in Cellular Networks 127

The basic idea the Equivalent Random Traffic (ERT) method is to use Erlang-B formula for overflow traffic offered to a secondary channel group of infinite capacity (Fig. 16), the so-called Kosten model (Kosten, 1937). Kosten's paper contains formulae for all binomial moments of number of busy channels in secondary overflow group. In practice, only the two first moments are used for characterization of the overflow traffic: mean traffic intensity *M* and variance *V*

### **5.2 Erlang formula and its generalization for non-integer number of channels**

We consider a system of *N* identical fully accessible channels (servers, trunks, slots, call center agents, pool of wavelengths in the optical network, etc) offered Poisson traffic *L* and operating as a loss system (blocked calls cleared). The probability that all *N* channels are busy at a random point of time is equal to:

$$B = E(N, L) = \frac{\frac{L^N}{N!}}{\sum\_{i=0}^{N} \frac{L^i}{i!}} \tag{6}$$

This is the famous Erlang-B formula (1917) (Iversen, 2011). For numerical analysis of (6) we use the well-known recurrent formula:

$$E(N+1,L) = \frac{L \cdot E(N,L)}{N+1+E(N,L)}\tag{7}$$

with initial value*E*(0, *L*) = 1. For the ERT-method we need Erlang-B formula for non-integral number of channels. How to get solution for a non-integral *N*? The traditional approach is based on the incomplete gamma function using:

$$E(N,L) = \frac{L^N \cdot e^{-L}}{\Gamma(N+1,L)}\tag{8}$$

where

$$
\Gamma(N+1, L) = \int\_L^{\infty} t^N \, e^{-t} \mathbf{d}t
$$

We propose a new approach for engineering applications. Let the value *N* be from the interval (0,1). We introduce a parabolic approximation for ln *R* = *lnE*(*N*, *L*) at points *N* = 0, *N* = 1, and *N* = 2:

$$\begin{aligned} \ln E(0, L) &= \ln(1) = 0 \\ B &= \ln E(1, L) = \ln \frac{L}{L + 1} \\ \text{C} &= \ln E(2, L) = \ln \frac{L^2}{L^2 + 2L + 2} \end{aligned}$$

Then we get the requested approximation:

$$
\ln E(\mathbf{N}, L) = \left(\frac{\mathbf{C}}{2} - B\right) \mathbf{N}^2 + \left(2B - \frac{\mathbf{C}}{2}\right) \mathbf{N}
$$

or in a more convenient form

$$E(N,L) = L^N \left(L+1\right)^{N^2-2N} \left(L^2+2L+2\right)^{\frac{N-N^2}{2}}\tag{9}$$

Thus we have an initial value of *E*(*N*, *L*) for *N* inside the interval (0,1) and we may calculate *E*(*N*, *L*) at any *N* by means of recurrent formula (7). In (Schneps-Schneppe & Sedols, 2010) the proposed approximation (6) is compared numerically with earlier known Erlang-B formula approximations (Rapp, 1964); (Szybicki, 1967); (Hedberg, 1981) and it is shown to be more accurate.

16 Will-be-set-by-IN-TECH

We consider a system of *N* identical fully accessible channels (servers, trunks, slots, call center agents, pool of wavelengths in the optical network, etc) offered Poisson traffic *L* and operating as a loss system (blocked calls cleared). The probability that all *N* channels are busy at a

This is the famous Erlang-B formula (1917) (Iversen, 2011). For numerical analysis of (6) we

*E*(*N* + 1, *L*) = *L* · *E*(*N*, *L*)

with initial value*E*(0, *L*) = 1. For the ERT-method we need Erlang-B formula for non-integral number of channels. How to get solution for a non-integral *N*? The traditional approach is

*E*(*N*, *L*) = *LN* · *e*−*L*

We propose a new approach for engineering applications. Let the value *N* be from the interval (0,1). We introduce a parabolic approximation for ln *R* = *lnE*(*N*, *L*) at points *N* = 0, *N* = 1,

*B* = ln *E*(1, *L*) = ln *L*

*C* = ln *E*(2, *L*) = ln *L*2

*L* + 1 ,

*L*2 + 2*L* + 2 .

*N*−*N*2

2 (9)

 ∞ *L t N e* −*t* d*t*

Γ(*N* + 1, *L*) =

ln *E*(0, *L*) = ln(1) = 0 ,

 *C* 2 − *B N*2 + 2*B* − *C* 2 *N*

*E*(*N*, *L*) = *LN* (*L* + 1)*N*2−2*N*(*L*2 + 2*L* + 2)

Thus we have an initial value of *E*(*N*, *L*) for *N* inside the interval (0,1) and we may calculate *E*(*N*, *L*) at any *N* by means of recurrent formula (7). In (Schneps-Schneppe & Sedols, 2010) the proposed approximation (6) is compared numerically with earlier known Erlang-B formula approximations (Rapp, 1964); (Szybicki, 1967); (Hedberg, 1981) and it is shown to be more

ln *E*(*N*, *L*) =

*LN N*! ∑*N i*=0 *Li i*!

*N* + 1 + *E*(*N*, *L*) (7)

Γ(*N* + 1, *L*) (8)

(6)

**5.2 Erlang formula and its generalization for non-integer number of channels**

*B* = *E*(*N*, *L*) =

random point of time is equal to:

use the well-known recurrent formula:

where

and *N* = 2:

accurate.

based on the incomplete gamma function using:

Then we get the requested approximation:

or in a more convenient form

Fig. 16. Kosten's model: *N* fully accessible channels and a channel group of infinite capacity.

## **5.3 Kosten's model and its new interpretation**

The basic idea the Equivalent Random Traffic (ERT) method is to use Erlang-B formula for overflow traffic offered to a secondary channel group of infinite capacity (Fig. 16), the so-called Kosten model (Kosten, 1937). Kosten's paper contains formulae for all binomial moments of number of busy channels in secondary overflow group. In practice, only the two first moments are used for characterization of the overflow traffic: mean traffic intensity *M* and variance *V* (reference is usually made to Riordan's paper (Riordan, 1956)):

$$M = L \cdot E(N, L) \tag{10}$$

$$V = M \cdot \left(1 - M + \frac{L}{N + 1 - L + M}\right) \tag{11}$$

From these two parameters one introduce a new parameter *Z*, the so-called peakedness:

$$Z = \frac{V}{M} = \left(1 - M + \frac{L}{N + 1 - L + M}\right) \ge 1\tag{12}$$

Experience shows that peakedness *Z* is a very good measure for the relative blocking probability a traffic stream with given mean value and variance is subject to.

We offer a new interpretation of Kosten's results. We consider the scheme in Fig. 17. There are *N* common channels and one separate channel. From (10 and (11 we get a new formula for the variance *V* when both mean *M* and mean *M*1 = *L* · *E*(*N* + 1, *L*) are known. From recurrence

$$L \longrightarrow \begin{array}{c} \hline N \\ \hline \end{array} \xrightarrow{M} \begin{array}{c} \begin{array}{c} M \\ \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} M\_1 \\ \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \hline \end{array} \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \hline \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \hline \end{array} \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \begin{array}{c} \hline \end{array} \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \begin{array}{c} \hline \end{array} \end{array} \xrightarrow{M\_1} \begin{array}{c} \begin{array}{c} \text{ $M\_1$ } \\ \hline \end{array} \end{array} \xrightarrow{}$$

Fig. 17. An illustration of the ERT-method extension.

formula (7) follows:

$$M\_1 = \frac{L\,M}{N + 1 + M}$$

or

$$M + N + 1 = \frac{L \, M}{M\_1}$$

After substitution of (*M* + *N* + 1) into (11) we get:

$$V = M \cdot \left(1 - M + \frac{L}{\frac{LM}{M\_1} - L}\right) = M\left(1 - M + \frac{M\_1}{M - M\_1}\right) \tag{13}$$

We can reduce this expression to a simpler form:

$$V = M^2 \left( \frac{1}{M - M\_1} - 1 \right) = M \left( \frac{M}{M - M\_1} - M \right) \tag{14}$$

Note that *M* − *M*1 is the load carried by a single channel and therefore it is always less than one. Formula (14) is useful for applications of the ERT-method in case of traffic splitting.

................................................................. ........ ..........................

lines (parameters: *L* = 5, *N* = 2).

.......................................................................................................................... ........ .......................... 3

process, and is equal to 4.30349, i.e. the relative error is less than 1%.

(in brackets) is 6.91011, i.e. the relative error again is less than 1%.

................................................................................................................................................................................................................. ........

1. First step is a direct application of formulae (10) (11) for two streams and two individual

2. The second step, we apply the formulae (10) (11) to a three-channel group. We get the equivalent parameters *L* = 9.265, *N* = 5.83071 and the lost traffic *M* = 4.3224. The exact value given in brackets is obtained by solving the system of equations of the Markov

3. Third step: The two overflow streams are fed into the two lines. We get the equivalent parameters: *L* = 16.2076, *N* = 10.3188, and the lost traffic *M* = 6.97707. The exact value

The results of calculations show the excellent accuracy of the method. However, such accuracy is not preserved when the number of channels in the third step increases. If instead of two channels we have (2 + *g*) channels in the common group, then Table 2 shows values of the loss for different *g* values. It is obvious the accuracy drops when increasing *g*. For a value of *g* = 10, the relative error is bigger than 3%, but always on the safe side, and the absolute loss

> *g* Loss probability Loss probability Relative error Exact ERT-method % 0.4083 0.4105 0.539 0.3239 0.3277 1.173 0.2459 0.2506 1.911 0.1765 0.1812 2.663 0.1181 0.1218 3.133 0.0724 0.0747 3.177

In (Fredericks, 1980) an equivalence method is proposed which is simpler to use than Wilkinson-Bretschneider's method. The motivation for the method was first put forward by W.S. Hayward. For given values of (*M*, *V*) of a non-Poisson flow, Frederick & Hayward's approach implies direct use of Erlang's formula *E*(*N*, *M*), but with scaling of its parameters

as *E*(*N*/*Z*, *M*/*Z*). The scaling parameter *Z* = *V*/*M* is the peakedness (12) (Fig. 20).

................................................................................................................................................................................................................. ........

M = 4.3224 exact 4.30349 L = 9.265 N = 5.83071

Call Admission Control in Cellular Networks 129

..........................

2

................................................................................................................................................................................................................. ........

M = 6.97707 exact 6.91011 L = 16.2076 N = 10.3188

..........................

.........................

3

........................................................................................................................... ........ ..........................

........................................................................................................................... ........ .........................

........................................................................................................................... ........

.................................................................. ........ ..........................

.................................................................. ........ ..........................

5

5

5

Fig. 19. Three-tier network.

probability is very small.

.................................................................. ........ ..........................

.......................... 5

2

2

2

2

Table 2. On accuracy of the classical ERT-method.

**5.6 Fredericks & Hayward's ERT-method**


**5. Can Eros survive?**

'Eros' is not consensually understood. In at least some readings, Plato is said to have endowed Eros with a non-erotic character; this idea is still with us in the notation of a 'platonic friendship.' In eighteenth and nineteenth century literature, Eros was about romance; this idealization of dyadic union is still with us too. In most recent theoretical accounts, Eros is distinguished from what Freud termed libido, from an erotic definition. Differentiating matters further, Jung considered Eros a 'feminine principle' and contrasted this energy with logos, the 'masculine principle.' Taking Eros into a different register, Marcuse stated in *Eros and Civilization* that '(t)oday the fight for life, the fight for Eros, is the *political* fight' [5]. On even the most basic plane of interpretation it is clear Eros can have different faces.

A working sense of Eros can be convened if three themes are clustered: life force; will to live; the desire for wholeness – for psychic relatedness. This clustering allows the question to be asked: can the life force, the will to live and the desire for wholeness survive the cart-wheeling impacts of climate collapse?

In one scenario, there is hope. In this scenario, if we can hold against emotional contagion there will continue to be a viable space for the life force, the will to live and the desire for wholeness. This 'holding against' stance is not the same as denying, or raging at, the tumult; nor is it about bowing down and surrendering. It involves the 'emotional literacy' [19] to name, and look directly at, fear and loss, guilt and anger, in order to make the place for what is positive and unwritten, for what is bright and fruitful.

In this approach loss needs to be honored. It will be ongoing, progressive and profound. Collective rituals of grieving, of propitiation, of gratitude, are part of a loving relationship with the earth as our mother. Although it will long remain unclear how much we have lost, mourning cannot be permanently deferred by putting this distressing emotion into an 'in-abeyance' status [20].

In this formulation psychic wholeness is not only about the integration of divergent intra-psychic energies. What profound wholeness requires is that the anthropogenic world view is de-centered. That is, psychic relatedness is a holistic aspiration linked to the motif of eco-feminism [13] more than the narrower images of Eros put forward by Plato, Freud, Marcuse and, in a somewhat differing way, to Jung. In this sense, the 'will to live' is re-formulated as the will to holistically connect and be at-one.

In the other scenario there is only a scant hope for Eros. In this misogynist worldview we will continue to beat our mother – to befoul, to strip mine, to vandalize. Disavowed programs of revenge, along with spontaneously erupting individual acts of revenge, will be enacted against mother earth because we are angry that she is wounded *and* that she will not continue to allow us to hurt her without sanction. This is not about the will to live but it's opposite [16]. Misogyny cannot understand that everything that lives exists interdependently. Nothing stands, or falls, on its own.

Eros' existential condition is relationality, as is yours and mine. The us and the we, if the understanding goes beyond the anthropogenic, are part of, not separate to, a

larger ecology. Adonis may have a seasonal lifecycle, a rhythm that climate collapse has disrupted, but he, like all beings, has a life that is in-relation. The grand seasonal tides may be disturbed but, however tragic this is, there will remain a larger living matrix.


**5. Findings**

This section presents analyses of the field data obtained from our investigation on the extent to which university teachers in a developing country like Ghana effectively used their digital leadership role to promote the use of digital technology in instructional delivery. The presentation in this section is based on only the quantitative aspect of the embedded/nested mixed-method design because, as indicated in Section 4 (see Subsection 4.3), the qualitative data did not yield new issues remarkably different from the main quantitative data.

*Improving Higher Education Instructional Delivery in the Developing World: The Role… DOI: http://dx.doi.org/10.5772/intechopen.100546*

The first research question asks: What kinds of digital technology tools do university teachers in Ghana often apply in their instructional delivery as digital leaders?

**Table 2** presents the means (*M*) and standard deviations (SD) of the various digital technology tools used for teaching in Ghanaian universities. It further shows the frequency (in mean rank) of their usage for instructional delivery. Looking at the measure of central tendencies on the table, an examination of only the extreme measures of the means (i.e., variable with the highest mean and the variable with the lowest mean) shows that the participants' responses for the variable "laptops" has the highest mean score (*m* = 4.28) indicating a skew toward the agreement scale, responses for the variable "digital speakers" has the lowest mean score (*m* = 1.52) indicating a skew toward the disapproval scale. By implication, whereas laptops were the most frequently used digital technology tool by the participants, digital speakers were the least used by them.

Having examined the measure of central tendencies on the table, it is equally important to also consider the measure of dispersion. From the table, two extreme measures of standard deviations (i.e., the most dispersed variable from its mean and the least dispersed variable from its mean) show that whereas responses for the variable "Television sets" are the farthest apart and most dispersed (SD = 1.63). On the opposite, responses for the variable "Digital cameras" are the closest and least dispersed (SD = 1.08).

The second research question also asks: How effective are digital technology tools in promoting academic work in Ghanaian universities?

**Table 3** shows respondents' opinions regarding the effectiveness of digital technology tools in promoting teaching and learning in Ghanaian universities. For the measure of central tendencies, an examination of only the extreme measures of the means gives the impression that while the responses for the variable contending that digital technology tools allowed students to easily retain and recollect learning concepts have the highest mean score (*m* = 4.51) indicating a skew toward the strong agreement scale, responses for the variable claiming that digital technology tools made it easy to attract students' attention has the lowest mean score (*m* = 2.20) manifesting a skew toward the disagreement scale.

In the case of the measure of dispersion, a perusal of the table also reveals two extreme measures of standard deviations showing that responses for the variable on the assertion that digital technology tools enhanced the learning experience of

#### **Table 2.**

*Kinds digital technology tools frequently used by Ghanaian universities.*

#### **Table 3.**

*Effectiveness of digital technology tools in promoting instructional delivery.*

students are the farthest apart and most dispersed (SD = 1.51). On the contrary, responses for the variable on the view that digital technology tools enabled students to easily retain and recollect learning concepts are the closest and least dispersed (SD = 0.88).

#### **6. Discussion**

The study found high average usage for laptops, mobile, and phones as the usually used educational technology for teaching by the participants in playing their roles as digital leaders. The study, however, revealed the speaker as the least used educational technology device by the teachers. The findings further show that although most of the participants used digital technology tools, only a few effectively applied the tools in their instructional delivery. This result is interesting, given that a burgeoning body of the literature such as Amory [23], Bagheri et al. [24], Bates and Sangra [25], and Cheung and Slavin [26] found their integration into instructional delivery of most higher education institutions in the world. These digital technology tools are common, easy to operate, accessible, and have numerous advantages [40]. These merits perhaps account for the reason most teachers would want to use them to teach. Alkash and Al-Dersi [29] and Chevers and Whyte [19] believed that these tools make teaching lively, less stressful, and flexible.

Another possible explanation could be that most of the university teachers owned laptops and mobile phones and used them for various social activities. Research has shown that most university teachers believe that the use of these technology tools enhances teaching and fosters collaboration between students and faculty members. For instance, Tondeur et al. [21] pointed out that educational technology integration in the instructional process has become common because technology has assumed a pivotal role in enhancing teaching and learning. It is therefore not surprising that the participants deployed the digital devices to teach. It is also believed that most students have laptops and mobile phones; therefore, the participants would naturally find it ideal to share information and educational resources with the students [5].

Again, the use of the digital tools in teaching by the participants as digital leaders appears to give credence to the finding that digital technology tools increase

#### *Improving Higher Education Instructional Delivery in the Developing World: The Role… DOI: http://dx.doi.org/10.5772/intechopen.100546*

teachers' ability to speedily search for information [2, 7] and library databases [1]. By implication, the findings mean that if university teachers in the developing world are assisted with technology devices, it would boost their morale and encourage them to integrate educational technology into teaching [41]. It would also enhance the quality of their instructional delivery and impact positively on students' learning outcomes.

Comparing the findings of this research with previous studies reveals some consistencies. For example, the findings appear to confirm a key finding of Bozalek et al. [1] who revealed that educational technology devices are used in teaching in higher institutions in South Africa. Furthermore, Sife et al. [6] found that in Tanzania, higher institutions faculty members use educational technology for many educational purposes. The findings also corroborate Jackson and Chapman's [4] research who reported that most lecturers were proficient in using PowerPoint and Word applications for teaching.

Arguing from the perspective of Roger's [42] diffusion of innovation theory, which anchors this research, one would reason that if digital technology tools are not easy to use, not accessible, and do not offer relative advantages to university teachers in the developing world, they might decline their usage in teaching. It appears obvious that university teachers in Ghana, like all other teachers in the higher education sector in the developing world, may have positive attitudes toward the usage of digital technology tools in teaching but they need support to procure them.

#### **7. Conclusion and recommendations**

This study aimed to use the context of Ghana to examine the kinds of digital technology tools university teachers in the developing world often used in their teaching as digital leaders, and whether the tools were effective in promoting academic work. The study mainly found laptops, mobile phones, and projectors as the commonest digital technology tools used in teaching by the participants, and they thought that the tools effectively promoted academic work.

The study has two major limitations. First, the accidental technique used to recruit participants from Ghanaian universities does not allow the findings to generalize beyond the present sample. Second, because the study used the cross-sectional survey design, it cannot offer causal interpretations. Based on the limitations, we recommend that future research should compare the experiences of university teachers playing digital leadership roles in different geographical contexts across several developing countries. Because the research found that digital technology tools were effective in promoting academic work, we wish to also suggest that universities in Ghana should provide allowances to teachers to assist them to procure digital technology tools needed for teaching.

Despite the limitations, substantially, the findings are original because, to the best of our knowledge, there has not been previous research that has focused on the same issue on HEIs in the developing world. The study is also novel because of its use of the Diffusion of Innovation (DoN) theory to discuss pertinent issues about digital technology use in higher education. Most importantly, because developing countries arguably have similar characteristics, the findings may apply favorably with other developing countries.

Again, the findings may have global implications because knowing the effectiveness of the use of digital technology in higher education teaching in Ghana as a developing country could serve as a source of information on measures HEIs in developing countries have put in place to deal with the negative impact of the Covid-19 pandemic on academic work. Finally, research also contributes to existing knowledge about how HEIs in the developing are using digital leadership to address the issue of large class size teaching bedeviling most universities.


**Abstract**

Drought can be referred to as a meteorological period without significant rainfall and it is one of such major abiotic stresses that contributes to a huge reduction in crop yield throughout the world. Plant shows a broad range of physiological, morphological, and biochemical changes such as reduced photosynthetic accumulation, altered gene expression, etc. Under the drought stress which ultimately causes reduced growth as well as poor grain yield. Drought stressconditions trigger production of ROS, which disrupts the dynamic balance between ROS production and ROS scavenging systems and its accumulation depends on the intensity as well as duration of water stress, and it varies among species. A plant species that has a better inherited genetic response allowing it to rapidly deploy its antioxidant enzymatic and non-enzymatic defense system, can tolerate drought better than a plant species with a poor antioxidant defense system. Furthermore, enzyme and protein encoding drought specific genes have the ability to enhance drought tolerance. These two enzymatic and genetic engineering strategies are unique and vital tools, which can be used to help alleviate the world's future problems related to energy, food, and environmental stresses, particularly drought. This chapter attempts to discuss developments in understanding effects of drought stress and underlying mechanisms in plants for its alleviation.

**Keywords:** ABA signaling, antioxidant, drought, ROS, stress


*2.2. Cocrystal Screening*

Compounds were prepared by cocrystallization via solvent-drop grinding: Stoichiometric amounts of **DL-H2ma** with **pic**, or **inam** were ground with a mortar and pestle for ca. 5–7 min with the addition of 10 μL of Ethyl acetate per 50 mg of cocrystal formers. The clear, nonsaturated resulting solutions were poured into a 5 mL vial and left to evaporate slowly under ambient conditions until cocrystals formed. The single crystals of (**pic**)-(**D-H2ma) (1**) and (**inam**)-(**L-H2ma) (3**), suitable for X-ray diffraction studies, were obtained in 2–15 days from Ethyl acetate.

Preparation of the cocrystals was also attempted with other solvents, such as dichloromethane, formamide, DMF, chloroform, acetonitrile, isopropyl alcohol, cyclohexane, ethanol, methanol, CCl4, THF and toluene, but the yield was lower.


**3. Catalytic processes**

Catalysts are mainly classified as homogeneous or heterogeneous groups. Homogeneous catalysts act in the same phase as the reaction mixture, whereas heterogeneous catalysts act in a different phase from the reaction mixture. Heterogeneous catalysts have the advantage of easy separation and reuse due to the advantage of being in a different phase from the reaction medium. Presently, the biodiesel industry is dominated by homogeneous catalytic applica‐ tions due to the basic convention and less time required for the conversion of oils to their respective methyl esters. NaOH and KOH are mainly used because they are easily soluble in methanol, forming sodium and potassium methoxide respectively while enhancing transes‐ terification reactions to completion. When the acid value (AV) of the oil is high, an acid catalyst such as hydrochloric acid or sulfuric acid is used to lower the AV and then the alkali catalyst is utilized for biodiesel synthesis.

### **3.1. Homogeneous/heterogeneous catalysis**

Conventional biodiesel production is done via base-catalysed transesterification using homogeneous alkaline catalysts. This is the most commonly used technique as it is considered to be the most economical process [31]. The fact that homogeneous catalysts cannot be reused makes conventional heterogeneous catalysis preferable as it offers a series of advantages among which are easy separation of products, reusability of catalysts and reduced amount of waste water during biodiesel production. Heterogeneous catalysis is thus considered to be a green process, as the purification steps of products are much more simplified with high yields of methyl esters being achieved [32]. Economically, heterogeneous catalysis poses a better alternative with regards the reduction of the costs associated with the purification and separation of reaction products in biodiesel production. This development could lead to a costeffective process that is environmentally friendly. For example, the high catalytic activity of calcium containing natural and biological materials (eggshells, limestone calcite, cuttlebone, dolomite and hydroxyapathite) for transesterification reactions have been reported [33]. Waste coal fly ash-based catalysts and fly ash-based zeolites have also proved to be successful catalysts and offer an alternative route for the utilization of South African fly ash in transes‐ terification reactions for biodiesel production [34, 35].

### **3.2. Process systems**

communities to natural ecosystems implying that that there may be significant linkages

Biodiesel presents the following advantages: improved fuel performance, increased lubricity, higher cetane number and flashpoint as compared to fossil-diesel, lower toxicity to living organisms, reduced exhaust emissions, and its versatility for use as fuel [22–25]. It is a local renewable source of energy that reduces importation of energy and affords improved security of energy supply. It is highly biodegradable [26, 27]. It also improves the quality of the environment with less harmful soot generated from the exhaust of vehicles. Biodiesel when mixed with fossil diesel creates a biodiesel blend suitable for diesel engines with minimal modifications as the superior lubricity of biodiesel increases functional engine efficiency; with a low viscosity and lower carbon monoxide emissions [28], biodiesel is user-friendly, non-toxic, and free of sulfur [29] and aromatics [30]. Possessing a high flash point eases storage and the presence of a higher amount of oxygen in biodiesel fuel guarantees the complete combustion of hydrocarbons. The production of biodiesel (methyl esters) from vegetable oils represents an alternative means of producing liquid fuels from biomass, and one which is growing rapidly in commercial importance and relevance due to the fluctuations in petroleum prices and the environmental advantages the process offers. Biodiesel can be produced from a variety of feedstock, vegetable oils, waste cooking oils and animal fats. These oils typically consist of C14– C20 fatty acid triglycerides. In order to produce a fuel that is suitable for use in diesel engines, these triglycerides are usually converted into the respective mono alkyl esters by base-

between the environmental and socioeconomic performance of biofuel projects [21].

catalyzed transesterification with short chain alcohol, usually methanol.

Catalysts are mainly classified as homogeneous or heterogeneous groups. Homogeneous catalysts act in the same phase as the reaction mixture, whereas heterogeneous catalysts act in a different phase from the reaction mixture. Heterogeneous catalysts have the advantage of easy separation and reuse due to the advantage of being in a different phase from the reaction medium. Presently, the biodiesel industry is dominated by homogeneous catalytic applica‐ tions due to the basic convention and less time required for the conversion of oils to their respective methyl esters. NaOH and KOH are mainly used because they are easily soluble in methanol, forming sodium and potassium methoxide respectively while enhancing transes‐ terification reactions to completion. When the acid value (AV) of the oil is high, an acid catalyst such as hydrochloric acid or sulfuric acid is used to lower the AV and then the alkali catalyst

Conventional biodiesel production is done via base-catalysed transesterification using homogeneous alkaline catalysts. This is the most commonly used technique as it is considered

**2.1. Biodiesel**

42 Biofuels - Status and Perspective

**3. Catalytic processes**

is utilized for biodiesel synthesis.

**3.1. Homogeneous/heterogeneous catalysis**

### *3.2.1. Ultrasound technology in biodiesel production*

Chemical reactions involving ultrasound homogenization/technology has been studied and has developed as an expanding research area in the field of biodiesel production. Utilizing ultrasound technology for producing biodiesel employs ultrasonic field known to produce chemical and physical effects that arise from the collapse of cavitation bubbles. The mixing intensity is considered a very important parameter to enhance a progressive transesterification reaction due to the fact that oil and alcohol are immiscible. Thus low-frequency sonication can be used to create emulsions from immiscible liquids and this effect could be employed for biodiesel preparation. The use of ultrasound technology has thus proven to provide a latent alternative to conventional biodiesel production process via transesterification by initiating substantial mechanical energy required for mixing and activating the transesterification reaction for biodiesel synthesis [36]. One major problem, however, in the transesterification of oils using methanol is the fact that alcohol is a poor solvent for fatty materials. Thus the need for homogenization of the reaction mixture since this reaction can only occur in the interfacial region between the liquids (due to the fact that fats and alcohols are not totally miscible); hence, the application of vigorous mixing is required to increase the area of contact between the two immiscible phases. This method of homogenization has been found successful for both biodiesel production batch processes and continuous operation [37–39]. The use of ultrasound technology in biodiesel production presents an efficient, swift and economically functional process with reduced reaction time, static separation time as well as generally higher yields over the conventional process [38, 40]. The effects of use of mechanical stirring, ultrasound technology and hydrodynamic cavitation on soybean biodiesel yields were studied under the following parameters (catalyst, KOH; feedstock, soybean oil; solvent, methanol; alcohol/oil molar ratio of 6:1, temperature of 45°C) by Lifka et al. [41] and Ji et al. [38]. Results obtained showed that the application of ultrasound technology enabled faster reaction periods and higher yields in comparison to mechanical stirring. Conversely, Thanh et al. [42] report identical biodiesel yields during alkaline transesterification of vegetable oils via both mechan‐ ical stirring and ultrasound processes using KOH. Employing ultrasound processes and solid catalysts in biodiesel synthesis as compared to conventional batch processes has also been found to reduce reaction time drastically. Kumar et al. [43] also report a 98.53% biodiesel yield when a combination of an ultrasound process and a solid catalyst is employed as compared to a conventional batch process. Figure 1 shows a schematic of biodiesel production using ultrasound technology. Production of biodiesel under the ultrasonic processing possess the following advantages, reduction in processing time, less amount of alcohol, minimal catalyst, faster separation time and reduced reaction temperature as revealed in several studies [37–43].

**Figure 1.** Schematic of biodiesel production using an ultrasound (Reproduced with permission from ©Hielscher Ultra‐ sonics)

### *3.2.2. Application of jet mixing in biodiesel production*

The application of jet mixing in commercial biodiesel production has been developed and applied to improve mixing and enhance mass transfer between vegetable oils/ animal fats with methanol/ethanol in stirred tank reactors in the presence of basic or acidic catalysts [44]. The challenges related to conventional biodiesel production processes include the limitation of reaction rate by mass transfer between triglycerides and alcohol due to factors of immiscibility; high conversion limitations in the absence of product removal since transesterification itself is a reversible reaction; and disadvantage of batch mode operations of small and medium-scale biodiesel commercial processes (against the advantages of continuous operation) necessary for the development of process intensification technologies. This technology has been applied successfully in South Africa for commercial biodiesel production and has recorded shorter reaction profile, low molar ratio of alcohol to oil as well as lower catalyst concentrations. The operating cost and energy consumptions required to purify biodiesel could be minimized while the recovery of excess alcohol and catalyst by-products during downstream processing made more efficient.

**Figure 2.** Stirred tank reactors using the jet mixing technology

### *3.2.3. Jet reactor*

identical biodiesel yields during alkaline transesterification of vegetable oils via both mechan‐ ical stirring and ultrasound processes using KOH. Employing ultrasound processes and solid catalysts in biodiesel synthesis as compared to conventional batch processes has also been found to reduce reaction time drastically. Kumar et al. [43] also report a 98.53% biodiesel yield when a combination of an ultrasound process and a solid catalyst is employed as compared to a conventional batch process. Figure 1 shows a schematic of biodiesel production using ultrasound technology. Production of biodiesel under the ultrasonic processing possess the following advantages, reduction in processing time, less amount of alcohol, minimal catalyst, faster separation time and reduced reaction temperature as revealed in several studies [37–43].

**Figure 1.** Schematic of biodiesel production using an ultrasound (Reproduced with permission from ©Hielscher Ultra‐

The application of jet mixing in commercial biodiesel production has been developed and applied to improve mixing and enhance mass transfer between vegetable oils/ animal fats with methanol/ethanol in stirred tank reactors in the presence of basic or acidic catalysts [44]. The challenges related to conventional biodiesel production processes include the limitation of reaction rate by mass transfer between triglycerides and alcohol due to factors of immiscibility; high conversion limitations in the absence of product removal since transesterification itself is a reversible reaction; and disadvantage of batch mode operations of small and medium-scale biodiesel commercial processes (against the advantages of continuous operation) necessary for the development of process intensification technologies. This technology has been applied successfully in South Africa for commercial biodiesel production and has recorded shorter reaction profile, low molar ratio of alcohol to oil as well as lower catalyst concentrations. The operating cost and energy consumptions required to purify biodiesel could be minimized while the recovery of excess alcohol and catalyst by-products during downstream processing

*3.2.2. Application of jet mixing in biodiesel production*

sonics)

44 Biofuels - Status and Perspective

made more efficient.

A variety of practical engineering applications have employed the use of impinging jets to enhance heat transfer due to the high local heat transfer coefficient it presents. These applica‐ tions include quenching of metals and glass, cooling of turbine-blades, cooling and drying of paper as well as the cooling of electronic equipment. Extensive review and numerous studies that expound heat transfer enhancement via impinging jets can be found in literature [45-47]. Jet rectors are reactors based on the impinging jet technology. This system provides intense mixing under pressure with different nozzle sizes which were specially developed for steel cutting (with water) and further developed for the mining sector especially in the goldmine sector of South Africa. The application of jet reactors as shown in Figure 2 was further developed in a continuous process reactor mainly for the production of biodiesel in South Africa by Nieuwoudt [48]. The jet-loop system (schematic shown in Figure 3) has been optimized in different studies and upscaled in medium- and large-scale biodiesel commercial plants in southern Africa [48].

### *3.2.4. Membrane technology*

Biodiesel production using membrane reactors is a new concept that is still being tested for optimal conditions. However, there are numerous prospects to finding paramount permuta‐ tion between catalyst and membrane. Design and optimization studies will be required to improve the membrane reactor for commercial small-scale operations, especially in sub-Saharan Africa. Therefore, a process is required to simultaneously overcome the shortcom‐

**Figure 3.** Schematic diagram of the jet reactor

ings of feedstock and the use of homogenous catalysts with the aid of membrane technology and heterogeneous catalysts. Different chemical reaction processes has been successfully applied using the membrane reactor technology, which suggests an apparent success in biodiesel production. The transesterification of lipids is a classic reversible chemical reaction that could also be combined with membrane reactor technology [49]. These membranes can be either organic in nature (i.e. polymeric) or inorganic, with inorganic membranes being better than the former in terms of their excellent thermal stability [50]. It has also been found out that different pore-sized membranes could retain canola oil using a reactor with a high purity of canola biodiesel obtainable. This method (as shown in Figure 4) has clear advantages over conventional means as it ends with a fatty acid methyl ester (FAME)-rich phase, a controlled contact of incompatible reactants, and an elimination of undesired side reactions. There is also an integration of reaction and separation into a single process, thereby reducing separation costs and recycle requirements, and an enhancement of thermodynamically limited or product inhibited reactions resulting in higher conversions. The FAME-rich phase still contains FAME, methanol, glycerol and water, causing a problem of downstream processing in terms of separation [50, 51]. This work by Dube et al. clearly demonstrates that a membrane reactor can be used to alleviate many of the difficulties highlighted and successfully carry out the transes‐ terification of lipids to biodiesel. Preliminary laboratory testing on the application of mem‐ brane technology has also been conducted by a group of researchers in South Africa. The process developed was able to simultaneously overcome the shortcomings of waste cooking oil feedstock and the use of homogenous catalysts with the aid of membrane technology and heterogeneous catalysts. The membrane was remarkably able to block un-reacted feed and impurities from entering the permeate, which gave rise to a higher purity FAME product [52].

#### *3.2.5. Reactive distillation technology*

Reactive distillation can be applied successfully in biodiesel production as a potent process intensification technique since the reactions leading to the end-product are controlled by chemical equilibrium. This process has been found to be highly advantageous in esterificationTechnologies for Biodiesel Production in Sub-Saharan African Countries http://dx.doi.org/10.5772/59859 47

**Figure 4.** Schematic diagram of a separation membrane reactor [52]

ings of feedstock and the use of homogenous catalysts with the aid of membrane technology and heterogeneous catalysts. Different chemical reaction processes has been successfully applied using the membrane reactor technology, which suggests an apparent success in biodiesel production. The transesterification of lipids is a classic reversible chemical reaction that could also be combined with membrane reactor technology [49]. These membranes can be either organic in nature (i.e. polymeric) or inorganic, with inorganic membranes being better than the former in terms of their excellent thermal stability [50]. It has also been found out that different pore-sized membranes could retain canola oil using a reactor with a high purity of canola biodiesel obtainable. This method (as shown in Figure 4) has clear advantages over conventional means as it ends with a fatty acid methyl ester (FAME)-rich phase, a controlled contact of incompatible reactants, and an elimination of undesired side reactions. There is also an integration of reaction and separation into a single process, thereby reducing separation costs and recycle requirements, and an enhancement of thermodynamically limited or product inhibited reactions resulting in higher conversions. The FAME-rich phase still contains FAME, methanol, glycerol and water, causing a problem of downstream processing in terms of separation [50, 51]. This work by Dube et al. clearly demonstrates that a membrane reactor can be used to alleviate many of the difficulties highlighted and successfully carry out the transes‐ terification of lipids to biodiesel. Preliminary laboratory testing on the application of mem‐ brane technology has also been conducted by a group of researchers in South Africa. The process developed was able to simultaneously overcome the shortcomings of waste cooking oil feedstock and the use of homogenous catalysts with the aid of membrane technology and heterogeneous catalysts. The membrane was remarkably able to block un-reacted feed and impurities from entering the permeate, which gave rise to a higher purity FAME product [52].

Reactive distillation can be applied successfully in biodiesel production as a potent process intensification technique since the reactions leading to the end-product are controlled by chemical equilibrium. This process has been found to be highly advantageous in esterification-

*3.2.5. Reactive distillation technology*

**Figure 3.** Schematic diagram of the jet reactor

46 Biofuels - Status and Perspective

type reactions with high free fatty acid feedstock [53]. The use of excess methanol becomes unnecessary with this method as this can shift the reaction equilibrium towards ester produc‐ tion by continuous removal of the water by-product [54]. An additional flash evaporator and a decanter are used to guarantee the high-purity biodiesel product from multiple feedstocks, which may include a biodiesel reactor, a decanter, a flash evaporator and a distillation column. Since methanol and water are much more volatile than the fatty ester and acid, these will separate easily at the top [55]. Researchers have considered various aspects of this technique, including optimization of reaction conditions, heat integration, use of thermally coupled distillation columns [56] as well as dual reactive distillation processes [56] with respect to biodiesel catalysis. A complex distillation column with a side rectifier has been shown capable of carrying out a reactive distillation process for the production of biodiesel as demonstrated in a novel integrated reactive separation process for FAME synthesis (Figure 5). This integrated biodiesel process is based on reactive separations powered by heterogeneous catalysts offering significant advantages such as minimal capital investment and operating costs, as well as limited catalyst-related waste streams and eliminating soap formation. This novel technology reported efficiently uses the raw materials (including low-cost feedstock, i.e. waste cooking oil) while considerably reducing the energy requirements for biodiesel production—85% lower compared to the baseline studies [57]. Using pure free fatty acid as feedstock, Kiss et al. [54] report an energy saving of up to 45% due to heat integration inclusion and a significant reduction in steam consumption in the raw material pre-heaters. This process presents the following advantages over conventional biodiesel production processes, shorter reaction time, high unit productivity, no additional alcohol requirement, lower capital and operational costs (due to the elimination of additional separation units as a single column is adequate), elimi‐ nation of neutralization and separation steps of catalysts when solid acid catalysts are used. It also offers additional substantial advantages, such as higher reaction rate and selectivity, avoidance of azeotropes and reduced energy consumption as well as solvent usage [51]. The apparent advantage that this method has over other methods of processing biodiesel stimu‐ lates further study, especially for continuous high-volume production in sub-Saharan Africa.

**Figure 5.** Schematic diagram of an integrated reactive separation process for FAME synthesis [57]

### **4. Feedstock: Potential and requirement**

A major strategy to ensure the sustainability and success of the biodiesel industry is reliant upon the availability of adequate supplies of reasonably priced feedstock. Raw material being the main driver in determining the total production cost of biodiesel, there is therefore need to focus attention on the type, availability and the use of raw materials (which include vegetable or animal fat and oils). Another issue to contend with is the competition between food production and energy production as strong views are expressed in research and academic circles. In order for the production of biodiesel to be sustainable in sub-Saharan Africa, the choice of feedstock must correlate with the availability of such in specific environ‐ ments. A brief biofuel potential in the SADC region of Africa is presented in Table 1. The various potential feedstock for biodiesel production that are currently available in southern Africa, which can be considered suitable, include but not limited to cashew nut, sesame seeds, castor oil, pumpkin, rapeseed, avocados, coconut, soybean, cotton seed, sunflower and maize. A highlight of a few of these feedstocks is done in the following sections.

**Table 1.** Status of biofuel potential in SADC region [58]

### **4.1. Canola**

following advantages over conventional biodiesel production processes, shorter reaction time, high unit productivity, no additional alcohol requirement, lower capital and operational costs (due to the elimination of additional separation units as a single column is adequate), elimi‐ nation of neutralization and separation steps of catalysts when solid acid catalysts are used. It also offers additional substantial advantages, such as higher reaction rate and selectivity, avoidance of azeotropes and reduced energy consumption as well as solvent usage [51]. The apparent advantage that this method has over other methods of processing biodiesel stimu‐ lates further study, especially for continuous high-volume production in sub-Saharan Africa.

48 Biofuels - Status and Perspective

**Figure 5.** Schematic diagram of an integrated reactive separation process for FAME synthesis [57]

A major strategy to ensure the sustainability and success of the biodiesel industry is reliant upon the availability of adequate supplies of reasonably priced feedstock. Raw material being the main driver in determining the total production cost of biodiesel, there is therefore need to focus attention on the type, availability and the use of raw materials (which include

**4. Feedstock: Potential and requirement**

Canola oil is an efficient biodiesel feedstock with excellent cold-flow properties due to the low saturation of its triglyceride content. About 44% oil can be extracted from the canola seed when crushed as compared to only 18 % for soybeans; a relatively popular biodiesel feedstock and 30% for sunflower. A great advantage that the use of canola as a feedstock source offers is that nothing goes to waste. The oil cake, which accounts for about 60% of the by-product, can be used as a protein-rich animal feed and the leftover glycerol from the oil can be used in producing soaps, cosmetics and other personal care products. ELIDZ is embarking on a canolabased biodiesel project together with the Eastern Cape Development Corporation, AsigSA along with the Department of Agriculture [59]. The project is expected to generate a number of jobs in the rural areas. Canola has the advantage of being a nitrogen-fixing winter crop, which can be alternated with maize, thereby increasing the maize yields, which would have the added benefit of increasing food security. On the topic of food security, a larger proportion of maize, which is currently excluded from biofuel production, can be utilized in the food production process as animal feed at a lower cost (since the raw product has already created other income and requires less transportation) and could additionally result in lower meat production costs.

### **4.2. Sunflower and soybean**

Other biodiesel feedstock sources include the sunflower and soybean and they are extensively cultivated in the South African Development Community (SADC) region of Africa. Their impact on employment is high as they are generally grown by both large and smallholder farmers for food crop or as industrial crops for small- and medium-scale enterprises in biodiesel [60]. This expanded use for the biodiesel industry creates surplus demand and encourages large productions. With respect to yields, sunflower offer greater yields but at a higher price than soybean as yields are highly influenced by seed selection, plant density as well as pest and weed control.

### **4.3. Jatropha**

Jatropha is regarded as a bio-energy feedstock without adequate scientific knowledge on the shrub and this has resulted in disappointed farmers in countries like Mozambique and Zambia, where out-grower schemes have failed [61]. Southern Africa's climatic conditions favour the production of a wide range of bio-fuel feedstocks but a study on preferred feedstocks carried out by WWF in five SADC countries prioritized Jatropha, sweet sorghum and sugarcane [62]. Jatropha was the most preferred feedstock largely because of its portrayal as a "miracle crop" that grows on marginal soils with limited to no management. The shrub grows wild but can be cultivated for bio-diesel production. It is drought tolerant, suited to well-drained soils and survives on a wide range of terrains and soil types. Its seed oil and other vegetative parts are, on the other hand, poisonous [63]; however, high temperature treatment can reduce toxicity. Oil from the seed can be processed into bio-diesel, soap and candles. In Malawi, Zambia and Zimbabwe, Jatropha is grown as a live fence/hedge by smallholder farmers. It has been established under plantation conditions by private companies in countries such as Mozambi‐ que and Tanzania. However, it poses a number of challenges. Its commercial cultivation is yet to take off and very few large-scale commercial plantations have been harvested, processed and reported [64]. It is worthy of mention that Jatropha's agronomic requirements, seed yields and economic returns are largely unknown. The political position towards Jatropha, however, has been strengthened by a national government initiative to support bio-diesel production while the benefits from job creation as well as the use of the end product could come in very positively, particularly for farmers. In addition to the oil produced from Jatropha, the cake remaining after the seeds are processed is a good organic fertilizer after composting it and can be used to make paper, cosmetics, toothpaste, embalming fluid, and cough medicine, among other items as well. One concern however is that the seeds are highly flammable and therefore the process should not be located near to any sugar or paper producing operations.

### **4.4. Algae**

used as a protein-rich animal feed and the leftover glycerol from the oil can be used in producing soaps, cosmetics and other personal care products. ELIDZ is embarking on a canolabased biodiesel project together with the Eastern Cape Development Corporation, AsigSA along with the Department of Agriculture [59]. The project is expected to generate a number of jobs in the rural areas. Canola has the advantage of being a nitrogen-fixing winter crop, which can be alternated with maize, thereby increasing the maize yields, which would have the added benefit of increasing food security. On the topic of food security, a larger proportion of maize, which is currently excluded from biofuel production, can be utilized in the food production process as animal feed at a lower cost (since the raw product has already created other income and requires less transportation) and could additionally result in lower meat

Other biodiesel feedstock sources include the sunflower and soybean and they are extensively cultivated in the South African Development Community (SADC) region of Africa. Their impact on employment is high as they are generally grown by both large and smallholder farmers for food crop or as industrial crops for small- and medium-scale enterprises in biodiesel [60]. This expanded use for the biodiesel industry creates surplus demand and encourages large productions. With respect to yields, sunflower offer greater yields but at a higher price than soybean as yields are highly influenced by seed selection, plant density as

Jatropha is regarded as a bio-energy feedstock without adequate scientific knowledge on the shrub and this has resulted in disappointed farmers in countries like Mozambique and Zambia, where out-grower schemes have failed [61]. Southern Africa's climatic conditions favour the production of a wide range of bio-fuel feedstocks but a study on preferred feedstocks carried out by WWF in five SADC countries prioritized Jatropha, sweet sorghum and sugarcane [62]. Jatropha was the most preferred feedstock largely because of its portrayal as a "miracle crop" that grows on marginal soils with limited to no management. The shrub grows wild but can be cultivated for bio-diesel production. It is drought tolerant, suited to well-drained soils and survives on a wide range of terrains and soil types. Its seed oil and other vegetative parts are, on the other hand, poisonous [63]; however, high temperature treatment can reduce toxicity. Oil from the seed can be processed into bio-diesel, soap and candles. In Malawi, Zambia and Zimbabwe, Jatropha is grown as a live fence/hedge by smallholder farmers. It has been established under plantation conditions by private companies in countries such as Mozambi‐ que and Tanzania. However, it poses a number of challenges. Its commercial cultivation is yet to take off and very few large-scale commercial plantations have been harvested, processed and reported [64]. It is worthy of mention that Jatropha's agronomic requirements, seed yields and economic returns are largely unknown. The political position towards Jatropha, however, has been strengthened by a national government initiative to support bio-diesel production while the benefits from job creation as well as the use of the end product could come in very

production costs.

50 Biofuels - Status and Perspective

**4.2. Sunflower and soybean**

well as pest and weed control.

**4.3. Jatropha**

Algae can be grown using waste materials such as sewage and without displacing land currently used for food production. The production of algae to harvest oil for biodiesel has not been conducted on a commercial scale, but the potential is promising. This "second generation" biodiesel feedstock has the potential to dramatically expand the resource base for the produc‐ tion of biodiesel in the future, hence contributing to solving complications of air pollution from CO2 and alleviating crises of food competition through energy production. A comprehensive study by Thurmond [65] found that algae and the potential of microalgae as an alternative and sustainable energy source) may offer an immense resolution to meeting large-scale and sustainable feedstock supply in developed continents like North America, Europe and Asia, which may thereafter be applicable to Africa. Recent reports show that a genetically engineered bacterium developed by scientists in the United States can produce ethanol biofuel from coarse, wild growing switch grass rather than using vital food crops such as maize [66].

### **4.5. Waste oil**

Recycled oil is a primary feedstock used for the production of biodiesel in most small- to medium-scale biodiesel plants in Southern Africa; however, its insufficient availability presents a big limitation to a large-scale biodiesel production process. Due to the numerous usages of recycled oil (e.g. yellow grease), its collection for use as various other potential feedstock (for the manufacture of soap, cleansing creams, inks, glues, solvents, paint thinner, rubber, lubricants and detergents) makes it highly competitive. The promising potential of using recycled oils as a livestock feed additive has also been identified as it makes livestock feed look fresh and lubricated while also reducing wear and tear on milling machinery. It is worthy of mention to note that apart from quantity constraint, the quality of the oil could have a knock on effect on the quality of the biodiesel produced [67]. A viable biodiesel industry in southern Africa cannot survive primarily on recycled oils due to their limited supply and availability. To meet the long-term feedstock need of the industry in southern Africa, a dedicated biodiesel oil-seed will need to be identified and developed. It is proposed that the best (monopolistic) feedstock should be one that empowers all players across the board from small to large, at the same time a breeding ground for sustainable biofuels industry.