**2.2 The study area**

The study was conducted in the semi-arid drylands of northern and central Tanzania, covering five districts namely Mwanga, Arusha, Babati, Kongwa and Ikungi from Kilimanjaro, Arusha, Manyara, Dodoma and Singida regions, respectively. The long-term temperatures and rainfall (1990–2016) were analyzed by considering averages of five base years from 1990 to 1995 and the five years of 2009–2016. The aim was to investigate if there was a notable shift in temperatures and rainfall in over the long-term. Unlike comparing a single base (1990) and terminal year (2016), the clusters of five years in the base and terminal periods facilitate capturing intermittent annual volatility.

The average annual monthly minimum temperature has increased by around 1°C in northern Tanzania (**Table 1**). This means that over time, months that used to be cooler are getting warmer. The maximum temperature has risen by 0.3°C in Arusha and 0.9°C in Mwanga. The standard deviations are larger, thus indicating increased volatility of temperatures. Rainfall has only increased marginally in Mwanga and Babati (+16.3 and + 1.3 mm, respectively) and decreased by 72.2 mm in Arusha. The magnitudes of standard deviations indicate higher inter-rainfall annual variability.

In the central semi-arid areas, in the cooler months, Kongwa has become warmer by about 1°C, while warmer months have registered an average increase of about 0.4°C over time (**Table 1**). Ikungi has registered marginally decreasing (−0.3°C) minimum and increasing (+0.1°C) maximum temperatures. In terms of


*Numbers in open and closed brackets are averages and standard deviations, respectively; na = data not available. ѱ Base earliest 5 years between 1990 and 1995. † The 5 years between 2009 and 2016.*

**133**

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists…*

distribution, which is what matters most for agricultural production.

rainfall, locations in central Tanzania have experienced an increase of monthly

Generally, over time, the study locations are getting warmer and experiencing an increase in rainfall. However, larger standard deviations, particularly with rainfall, indicate higher variability. Increasing temperatures will counteract marginal increases in rainfall through increased evapo-transpiration, thus inducing stress on plant and animal production. While our results highlight the general trend in temperature and rainfall, a more rigorous analysis of rainfall distribution in terms of the number of rainy days would provide more information on the rainfall

This study used a cross-sectional survey design that allowed data to be collected

( ) <sup>=</sup> <sup>+</sup> <sup>2</sup> <sup>1</sup> *N*

where *n* is the sample size; *N* is the population size; and *e* is the level of preci-

Using the above formula, a total of 411 agro-pastoral households were sampled. A multi-stage sampling approach was employed in order to select the study locations and, ultimately, the individual agro-pastoral households. The study locations from region, district, ward, and down to the village were selected based on the criteria of having the largest number of agro-pastoralists. The study covered 5 regions, each represented by one district, as well as an overall total of 12 wards and 23 villages. The last stage was random selection of agro-pastoral farmers from lists of heads of agro-pastoral households as provided by agricultural field officers in the

Both primary and secondary data are used in this study. Primary data were collected through structured household questionnaire interviews. The questionnaire covered among other information, the production costs, yields and sales. Secondary data were collected from respective offices at the meteorological weather stations, the National Bureau of Statistics (NBS) online database, and district offices. The secondary data included historical crop yields and livestock numbers, with the latter expressed in livestock units (LU). The conversion of different livestock species into a standard LU was based on the coefficients suggested by [24]: one animal representing 0.7, 0.1, 0.1 and 0.01 LU for cattle, goat, sheep, and chickens, respectively.

Beforehand, it was important to present the conventional MPT terminologies

*N e*

(1)

at a single point in time, while still having a broad scope. The study used crop livestock panel data over the period of 8 years (2009–2017), available at the district level, are also used in the analysis. A simplified formula for proportions by [8] is adopted in order to obtain the desired sample size of agro-pastoralists in semi-arid northern and central Tanzania, assuming 95% confidence level and 0.05 as sam-

*n*

*DOI: http://dx.doi.org/10.5772/intechopen.94133*

**2.3 Research design and sampling**

sion i.e. sampling error.

selected sample villages.

**2.5 Data analysis**

**2.4 Data types and data collection**

*2.5.1 Adapted definitions of MPT terminologies*

and how they were applied in this paper (**Table 2**).

pling error; the formula is as follows in Eq. 1:

annual average rainfall between 50 and 60 mm.

#### **Table 1.**

*Average monthly annual temperature and rainfall trend in the study areas.*

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists… DOI: http://dx.doi.org/10.5772/intechopen.94133*

rainfall, locations in central Tanzania have experienced an increase of monthly annual average rainfall between 50 and 60 mm.

Generally, over time, the study locations are getting warmer and experiencing an increase in rainfall. However, larger standard deviations, particularly with rainfall, indicate higher variability. Increasing temperatures will counteract marginal increases in rainfall through increased evapo-transpiration, thus inducing stress on plant and animal production. While our results highlight the general trend in temperature and rainfall, a more rigorous analysis of rainfall distribution in terms of the number of rainy days would provide more information on the rainfall distribution, which is what matters most for agricultural production.

#### **2.3 Research design and sampling**

*Agrometeorology*

tion in SSA.

**2.2 The study area**

facilitate capturing intermittent annual volatility.

Agro-pastoralists might wish to avoid income fluctuation and to maximize income at the same time. Since farms can be thought of as assets within an overall portfolio, agricultural producers might behave in a manner like investors who pay attention to the concept of diversification. However, agro-pastoralists might not have considered diversification in the same way that the typical financial investor might: they often look at diversification as changing their crop mix, rotational system, and livestock breeds, or even as cultivating spatially separated farms [22] and splitting livestock across distant grazing locations [23]. Despite the relevance of considering these forms of diversification, our analytical scope is limited to diversification of alternative combinations of crop and livestock enterprises. However, it highlights possible future research for increasing the scope of portfolio diversifica-

The study was conducted in the semi-arid drylands of northern and central Tanzania, covering five districts namely Mwanga, Arusha, Babati, Kongwa and Ikungi from Kilimanjaro, Arusha, Manyara, Dodoma and Singida regions, respectively. The long-term temperatures and rainfall (1990–2016) were analyzed by considering averages of five base years from 1990 to 1995 and the five years of 2009–2016. The aim was to investigate if there was a notable shift in temperatures and rainfall in over the long-term. Unlike comparing a single base (1990) and terminal year (2016), the clusters of five years in the base and terminal periods

The average annual monthly minimum temperature has increased by around 1°C in northern Tanzania (**Table 1**). This means that over time, months that used to be cooler are getting warmer. The maximum temperature has risen by 0.3°C in Arusha and 0.9°C in Mwanga. The standard deviations are larger, thus indicating increased volatility of temperatures. Rainfall has only increased marginally in Mwanga and Babati (+16.3 and + 1.3 mm, respectively) and decreased by 72.2 mm in Arusha. The magnitudes of standard deviations indicate higher inter-rainfall annual variability. In the central semi-arid areas, in the cooler months, Kongwa has become warmer by about 1°C, while warmer months have registered an average increase of about 0.4°C over time (**Table 1**). Ikungi has registered marginally decreasing (−0.3°C) minimum and increasing (+0.1°C) maximum temperatures. In terms of

] Now [recent 5 years†

Mwanga 17.0 (0.32) 29.9 (0.82) 18.1 (0.36) 30.7 (0.34) 431.9 (146.7) 448.2 (80.9) Arusha 14.4 (0.26) 26.0 (0.61) 14.9 (0.29) 26.3 (0.47) 775.1 (195.9) 702.8 (132.7) Babati na na na Na 584.4 (183.3) 585.7 (227.9) Kongwa 16.8 (0.40) 29.0 (1.28) 17.6 (0.14) 29.4 (0.19) 517.2 (30.0) 566.6 (168.5) Ikungi 16.5 (0.58) 27.5 (0.38) 16.2 (0.17) 27.6 (0.34) 608.5 (188.8) 668.8 (193.9) *Numbers in open and closed brackets are averages and standard deviations, respectively; na = data not available.*

**Temperature (°C) Rainfall (mm)**

] **Min Max Min Max**

] Base years [earliest 5 years<sup>ѱ</sup> ]

Now [recent 5 years†

**132**

*ѱ*

*†*

**Table 1.**

**Study locations**

District Base years [earliest 5 years<sup>ѱ</sup>

*Base earliest 5 years between 1990 and 1995.*

*Average monthly annual temperature and rainfall trend in the study areas.*

*The 5 years between 2009 and 2016.*

This study used a cross-sectional survey design that allowed data to be collected at a single point in time, while still having a broad scope. The study used crop livestock panel data over the period of 8 years (2009–2017), available at the district level, are also used in the analysis. A simplified formula for proportions by [8] is adopted in order to obtain the desired sample size of agro-pastoralists in semi-arid northern and central Tanzania, assuming 95% confidence level and 0.05 as sampling error; the formula is as follows in Eq. 1:

$$m = \frac{N}{1 + N\left(e^2\right)}\tag{1}$$

where *n* is the sample size; *N* is the population size; and *e* is the level of precision i.e. sampling error.

Using the above formula, a total of 411 agro-pastoral households were sampled.

A multi-stage sampling approach was employed in order to select the study locations and, ultimately, the individual agro-pastoral households. The study locations from region, district, ward, and down to the village were selected based on the criteria of having the largest number of agro-pastoralists. The study covered 5 regions, each represented by one district, as well as an overall total of 12 wards and 23 villages. The last stage was random selection of agro-pastoral farmers from lists of heads of agro-pastoral households as provided by agricultural field officers in the selected sample villages.

#### **2.4 Data types and data collection**

Both primary and secondary data are used in this study. Primary data were collected through structured household questionnaire interviews. The questionnaire covered among other information, the production costs, yields and sales. Secondary data were collected from respective offices at the meteorological weather stations, the National Bureau of Statistics (NBS) online database, and district offices. The secondary data included historical crop yields and livestock numbers, with the latter expressed in livestock units (LU). The conversion of different livestock species into a standard LU was based on the coefficients suggested by [24]: one animal representing 0.7, 0.1, 0.1 and 0.01 LU for cattle, goat, sheep, and chickens, respectively.

#### **2.5 Data analysis**

#### *2.5.1 Adapted definitions of MPT terminologies*

Beforehand, it was important to present the conventional MPT terminologies and how they were applied in this paper (**Table 2**).


#### **Table 2.**

*Terminologies of the MPT as applied in this paper.*

#### *2.5.2 Incorporating local management into district crop yields and livestock units*

The cross-sectional survey used in our study cannot generate longer time series data because even recalling data from five-years before is problematic as smallholder farmers do not keep records. In order to predict future (expected) returns for crop and livestock portfolios, frequently the historical performance of respective returns are examined [25, 26]. The secondary data for eight years (2009–2017) are the longest time series of crop yields and livestock numbers available from the study districts. These secondary data were the basis for simulating the distributions of future expected yields and livestock units.

Given the nature of data collection and management systems of family farms, the aggregated district-level data can have biases and errors resulting from the aggregation process. Common crop yield estimates are often based on rough estimates of aggregate production and area harvested. Owing to significant variation in farming practices and growing conditions across farming systems and agro-ecological zones, higher-level yield estimates may differ starkly from local yields realized by any given smallholder farmer [27]. According to [28], yields and related economic returns vary overtime and space, with this variation important for understanding the vulnerability of farms to climatic risks. Therefore, we used the cross-sectional household survey crop yields and livestock units to normalize respective district-level data. The normalization tends to localize aggregated data thus improving the reliability and validity of yields and livestock units at the local scale. The normalization approach is adapted from the work of [28] as follows in Eq. 2.

$$\mathcal{B}\_{a\circ} = \mathcal{y}\_{a\circ k} \land \mathcal{y}\_{hk} \tag{2}$$

**135**

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists…*

used to derive returns and associated risks as shown in Eq. 3.

The factor is used to downscale secondary aggregated crop yields and livestock units to the local context. Thus, the normalized crop yields and livestock units were

> *y y nij aij aijk* = × β

th year of reference production period.

The size of the returns was measured by the expected mean value of the distribution. The expected mean return *E*(*R*) is given by the weighted average of all possible returns, using the probability of achieving the actual return (*R*i). However, this study employs historical data, meaning that the expected or mean return *R*<sup>i</sup> of an individual agro-pastoral return is derived from normalized crop yields and livestock units during the 2009–2017 period. The expected returns of crops and

> = =∑1

period of a particular crop and livestock units per household over the 2009 to 2017

The risk of an individual agro-pastoral's crop and livestock portfolios was measured using respective standard deviations. For most agro-pastoralists as "investors," risk is experienced when they engage in crop and livestock production activities that generate returns that are lower than what was expected. As a result, it is a negative deviation from the expected (average) return. In other words, each crop and livestock activity presents its own standard deviation [26]. A higher standard deviation translates into greater risk. The standard deviation of a return is the square root of the variance. In general, the variance can be calculated as shown

( ) ( )

<sup>2</sup>= variance, *n* = number of year, *R*i = historical actual return, and *R*=

Regardless of how the individual return was calculated, the expected return of a portfolio is the weighted sum of the individual returns from the crops and livestock

> = =∑1

Where: *w*s = the proportion of the value of the portfolio constituted by the

*n p ss s*

*i*

= = −÷ <sup>∑</sup> <sup>2</sup> <sup>2</sup> 1

*i*

*n*

period; *R*s = historical actual returns, and *n* = number of years.

σ

= expected return of crop enterprise per hectare over the 2009 to 2017

*n i ss s*

*nij y* = normalized aggregate district-level yield/livestock type unit of *i*

(3)

*R pR* (4)

*RR n* (5)

*R wR* (6)

th crops or livestock entities, that is, it is the 'weight'

th

*DOI: http://dx.doi.org/10.5772/intechopen.94133*

*2.5.3 Expected returns of crop or livestock*

livestock are estimated as shown in Eq. 4:

*2.5.4 Risk of individual agro-pastoralist activities*

where

where *Ri*

in Eq. 5:

where σ

expected returns.

*2.5.5 Expected returns of a portfolio*

current market value of the *i*

that form portfolio, as presented in Eq. 6:

crop/livestock type in *j*

where *aijk y* = aggregate district-level yield/livestock specie unit of *i* th crop/ livestock type in *j* th year of the reference production period (2009–2017), in *k*th district; *hik y* = average observed household yield/livestock type unit of *i* th crop/livestock type in *k*th district; β *aij* = normalization factor of aggregate district level crop yield/livestock type unit of *i* th crop/livestock type in *j* th district.

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists… DOI: http://dx.doi.org/10.5772/intechopen.94133*

The factor is used to downscale secondary aggregated crop yields and livestock units to the local context. Thus, the normalized crop yields and livestock units were used to derive returns and associated risks as shown in Eq. 3.

$$\mathcal{Y}\_{a\psi} = \mathcal{B}\_{a\psi} \times \mathcal{Y}\_{a\psi k} \tag{3}$$

where *nij y* = normalized aggregate district-level yield/livestock type unit of *i* th crop/livestock type in *j* th year of reference production period.

#### *2.5.3 Expected returns of crop or livestock*

*Agrometeorology*

**Table 2.**

*2.5.2 Incorporating local management into district crop yields and livestock units*

**Terminology Usage in financial investments and in this paper**

agro-pastoralist.

livestock activities.

livestock portfolios.

higher returns at given levels of risk.

Asset/securities Items within a portfolio which refers to crops and livestock species kept by the

Correlation A measure of the degree to which the change in the return of two assets is similar. It represents correlations of crop and livestock activities. Diversification Investing in different assets that together make up a portfolio. It refers to crop and

Efficient portfolio A portfolio offering maximal expected return at a chosen level of risk or minimal risk

Return Financial returns from a financial investment asset. The concept refers to the value of crop outputs and livestock units managed by the agro-pastoralist. Risk The uncertain outcome of a financial investment expressed as standard deviations or

Portfolio Set of financial assets held by an investor. It refers to the crop and livestock combinations pursued by the agro-pastoralist.

at a given return. In this study, it is the crop and livestock portfolio giving relatively

variance. It refers to the standard deviations associated with returns from crop and

future expected yields and livestock units.

*Terminologies of the MPT as applied in this paper.*

adapted from the work of [28] as follows in Eq. 2.

β

district; *hik y* = average observed household yield/livestock type unit of *i*

β

*aijk y* = aggregate district-level yield/livestock specie unit of *i*

th year of the reference production period (2009–2017), in *k*th

th crop/livestock type in *j*

The cross-sectional survey used in our study cannot generate longer time series data because even recalling data from five-years before is problematic as smallholder farmers do not keep records. In order to predict future (expected) returns for crop and livestock portfolios, frequently the historical performance of respective returns are examined [25, 26]. The secondary data for eight years (2009–2017) are the longest time series of crop yields and livestock numbers available from the study districts. These secondary data were the basis for simulating the distributions of

Given the nature of data collection and management systems of family farms, the aggregated district-level data can have biases and errors resulting from the aggregation process. Common crop yield estimates are often based on rough estimates of aggregate production and area harvested. Owing to significant variation in farming practices and growing conditions across farming systems and agro-ecological zones, higher-level yield estimates may differ starkly from local yields realized by any given smallholder farmer [27]. According to [28], yields and related economic returns vary overtime and space, with this variation important for understanding the vulnerability of farms to climatic risks. Therefore, we used the cross-sectional household survey crop yields and livestock units to normalize respective district-level data. The normalization tends to localize aggregated data thus improving the reliability and validity of yields and livestock units at the local scale. The normalization approach is

= / *aij aijk hik y y* (2)

*aij* = normalization factor of aggregate district

th crop/

th

th district.

**134**

where

livestock type in *j*

crop/livestock type in *k*th district;

level crop yield/livestock type unit of *i*

The size of the returns was measured by the expected mean value of the distribution. The expected mean return *E*(*R*) is given by the weighted average of all possible returns, using the probability of achieving the actual return (*R*i). However, this study employs historical data, meaning that the expected or mean return *R*<sup>i</sup> of an individual agro-pastoral return is derived from normalized crop yields and livestock units during the 2009–2017 period. The expected returns of crops and livestock are estimated as shown in Eq. 4:

$$\overline{R\_i} = \sum\_{s=1}^{n} p\_s R\_s \tag{4}$$

where *Ri* = expected return of crop enterprise per hectare over the 2009 to 2017 period of a particular crop and livestock units per household over the 2009 to 2017 period; *R*s = historical actual returns, and *n* = number of years.

#### *2.5.4 Risk of individual agro-pastoralist activities*

The risk of an individual agro-pastoral's crop and livestock portfolios was measured using respective standard deviations. For most agro-pastoralists as "investors," risk is experienced when they engage in crop and livestock production activities that generate returns that are lower than what was expected. As a result, it is a negative deviation from the expected (average) return. In other words, each crop and livestock activity presents its own standard deviation [26]. A higher standard deviation translates into greater risk. The standard deviation of a return is the square root of the variance. In general, the variance can be calculated as shown in Eq. 5:

$$\sigma^2 = \left[\sum\_{i=1}^n \left(R\_i - \overline{R}\right)^2\right] \div \left(n\right) \tag{5}$$

where σ<sup>2</sup>= variance, *n* = number of year, *R*i = historical actual return, and *R*= expected returns.

#### *2.5.5 Expected returns of a portfolio*

Regardless of how the individual return was calculated, the expected return of a portfolio is the weighted sum of the individual returns from the crops and livestock that form portfolio, as presented in Eq. 6:

$$
\overline{R}\_p = \sum\_{s=1}^{n} w\_s \overline{R}\_s \tag{6}
$$

Where: *w*s = the proportion of the value of the portfolio constituted by the current market value of the *i* th crops or livestock entities, that is, it is the 'weight' attached to the crop or livestock, *n* = the number of crops or livestock in the portfolio, *Rs* = historical actual return, *Rp* = expected return of the portfolio.

#### *2.5.6 Risk associated with portfolio diversification strategy*

Risk is the chance that an investment's return will be different from what is expected. This includes the possibility of losing some or all of the original investment. Modern Portfolio Theory [16] offers a solution to the problem of portfolio choice for a risk-averse investor like an agro-pastoralist. The optimal portfolios, from the rational investor's point of view, are defined as those having the lowest risk for a given return. These portfolios are said to be mean–variance efficient [29]. Portfolio risk is measured by calculating the standard deviation of the historical returns or average returns of a specific investment. The standard deviation of the portfolio's rate of return depends on the standard deviations of return for its entities, their correlation coefficients, and the proportions invested. It is calculated as shown in Eq. 7:

$$
\sigma\_p = \sqrt{\sum\_{i=1}^{n} \sum\_{i=1}^{n} X\_i} X\_i X\_j \rho\_{\vec{y}} \sigma\_i \sigma\_j \tag{7}
$$

where σ *p* = standard deviation of the portfolio,

*X Xi j* = proportion invested in each asset,

ρ*ij* = correlation coefficients between *i* and *j*,

σ σ*i j* = standard deviation of each asset, and n = number of years.

#### **2.6 Results and discussion**

#### *2.6.1 Crops and livestock diversification in Tanzania*

Diversification is widely used strategy to spread risk as crops differ in their sensitivity to climate and weather extremes. About three-quarters (77%) of the responding agro-pastoral households diversified with two or more crops. A substantial number of agro-pastoral households only grow maize (**Figure 1(a)**). The eight widely diversified crop portfolios, with at least ten counts of involved agropastoral households, included maize-sunflower, maize-cowpea, maize-groundnut, maize-millet-sunflower, maize-pigeon pea, maize-millet, maize-sorghum, and maize-green gram. Maize as the main preferred staple features in all major crop portfolios. With exception of maize, which is relatively sensitive to seasonal droughts and intermittent dry spells, the other crops tolerate drought, a characteristic of the semi-arid farming environment. This means that agro-pastoralists in these dryland areas tend to make a strategic choice of crop mixes in order to minimize production risks. The high preference of maize as a food staple suggests that there must be efforts in crop breeding and improvement programs in order to develop drought efficient maize varieties.

Diversification involving two or more livestock types involved around 70% of the sampled agro-pastoralists. Chickens were kept by the majority of the agro-pastoralists (**Figure 1(b)**). The prominent livestock diversification portfolios included cattle-goat-chicken, cattle-goat, cattle-chicken, and goatchicken. Despite the fact that cattle production is highly vulnerable to seasonal droughts drying up pasture and water sources, it remains a part of the livelihood activities of agro-pastoral communities. Cattle are part of the social identity of

**137**

critical drought periods.

**Figure 1.**

ability to the drier environment.

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists…*

agro-pastoral societies in Tanzania. Goats, appearing in every major diversified livestock portfolio, are well adapted to the drier environment due to its ability to browse on trees and shrubs. Scavenging rural chickens can also thrive through

*Current diversification of crop and livestock portfolios. (a) Crop portfolio diversification. (b) Livestock* 

*portfolio diversification. (c) Crop-livestock portfolios diversification.*

Furthermore, diversification strategies integrating crops and livestock stand a better chance of effectively spreading production risk. Four major used diversified crop-livestock portfolios with at least ten counts of respondent agro-pastoralists include maize-millet-sunflower-cattle-goat-sheep-chicken, maize-cattle-goatchicken, maize-cattle, and maize-sunflower-cattle-goat-chicken. Maize also features in the integrated crop-livestock portfolios. Goat and chickens, which are relatively less affected by droughts, were part of the integrated portfolios. Goats can browse on shrubs when the land lacks grazing grasses due to seasonal droughts. Cattle and sheep, which are reliant upon grasses, are highly vulnerable when grazing grounds dry up due to prolonged seasonal droughts. The prominence of cattle that are less-resilient to critical droughts and stressful heat urges for interventions to reduce risk in cattle production. Such interventions include water harvesting and storage, as well as climate-smart breeding and genotype improvement for adapt-

*DOI: http://dx.doi.org/10.5772/intechopen.94133*

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists… DOI: http://dx.doi.org/10.5772/intechopen.94133*

#### **Figure 1.**

*Agrometeorology*

shown in Eq. 7:

where σ*p*

ρ*ij*

σ σ

**2.6 Results and discussion**

drought efficient maize varieties.

= historical actual return, *Rp*

*2.5.6 Risk associated with portfolio diversification strategy*

σ

= standard deviation of the portfolio,

*X Xi j* = proportion invested in each asset,

*2.6.1 Crops and livestock diversification in Tanzania*

= correlation coefficients between *i* and *j*,

= = <sup>=</sup> ∑∑1 1

*i j* = standard deviation of each asset, and n = number of years.

Diversification is widely used strategy to spread risk as crops differ in their sensitivity to climate and weather extremes. About three-quarters (77%) of the responding agro-pastoral households diversified with two or more crops. A substantial number of agro-pastoral households only grow maize (**Figure 1(a)**). The eight widely diversified crop portfolios, with at least ten counts of involved agropastoral households, included maize-sunflower, maize-cowpea, maize-groundnut, maize-millet-sunflower, maize-pigeon pea, maize-millet, maize-sorghum, and maize-green gram. Maize as the main preferred staple features in all major crop portfolios. With exception of maize, which is relatively sensitive to seasonal

droughts and intermittent dry spells, the other crops tolerate drought, a characteristic of the semi-arid farming environment. This means that agro-pastoralists in these dryland areas tend to make a strategic choice of crop mixes in order to minimize production risks. The high preference of maize as a food staple suggests that there must be efforts in crop breeding and improvement programs in order to develop

Diversification involving two or more livestock types involved around 70% of the sampled agro-pastoralists. Chickens were kept by the majority of the agro-pastoralists (**Figure 1(b)**). The prominent livestock diversification portfolios included cattle-goat-chicken, cattle-goat, cattle-chicken, and goatchicken. Despite the fact that cattle production is highly vulnerable to seasonal droughts drying up pasture and water sources, it remains a part of the livelihood activities of agro-pastoral communities. Cattle are part of the social identity of

*n n p i j ij i j i i*

lio, *Rs*

attached to the crop or livestock, *n* = the number of crops or livestock in the portfo-

Risk is the chance that an investment's return will be different from what is expected. This includes the possibility of losing some or all of the original investment. Modern Portfolio Theory [16] offers a solution to the problem of portfolio choice for a risk-averse investor like an agro-pastoralist. The optimal portfolios, from the rational investor's point of view, are defined as those having the lowest risk for a given return. These portfolios are said to be mean–variance efficient [29]. Portfolio risk is measured by calculating the standard deviation of the historical returns or average returns of a specific investment. The standard deviation of the portfolio's rate of return depends on the standard deviations of return for its entities, their correlation coefficients, and the proportions invested. It is calculated as

= expected return of the portfolio.

ρσσ

*X X* (7)

**136**

*Current diversification of crop and livestock portfolios. (a) Crop portfolio diversification. (b) Livestock portfolio diversification. (c) Crop-livestock portfolios diversification.*

agro-pastoral societies in Tanzania. Goats, appearing in every major diversified livestock portfolio, are well adapted to the drier environment due to its ability to browse on trees and shrubs. Scavenging rural chickens can also thrive through critical drought periods.

Furthermore, diversification strategies integrating crops and livestock stand a better chance of effectively spreading production risk. Four major used diversified crop-livestock portfolios with at least ten counts of respondent agro-pastoralists include maize-millet-sunflower-cattle-goat-sheep-chicken, maize-cattle-goatchicken, maize-cattle, and maize-sunflower-cattle-goat-chicken. Maize also features in the integrated crop-livestock portfolios. Goat and chickens, which are relatively less affected by droughts, were part of the integrated portfolios. Goats can browse on shrubs when the land lacks grazing grasses due to seasonal droughts. Cattle and sheep, which are reliant upon grasses, are highly vulnerable when grazing grounds dry up due to prolonged seasonal droughts. The prominence of cattle that are less-resilient to critical droughts and stressful heat urges for interventions to reduce risk in cattle production. Such interventions include water harvesting and storage, as well as climate-smart breeding and genotype improvement for adaptability to the drier environment.

#### *2.6.2 Crop and livestock production with associated risks*

#### *2.6.2.1 Localized crop yields with associated risks*

The district-level secondary data for seven growing seasons were localized through normalization with observed crop yields and livestock units in the survey localities. **Table 3** shows that, exception for millet, localized yields were higher than unadjusted aggregate district-level crop yields. For most crops, including sorghum, green gram, maize, groundnuts and beans, the localized yields ranged between a quarter and a half percent over the aggregate district-level yields.

The temporal volatility of locally adjusted crop yields, as reflected by the coefficients variation, was apparently higher than unadjusted district-level yields. Implicitly, local yields were highly variable with higher variances that were extended into adjusted aggregate yields through the normalization process. Although the aggregate data includes data collected outside the survey localities, it is still plausible to argue that the study locales represent the areas of respective districts. Therefore, in order to realistically reflect the local production risks faced by agro-pastoralists, downscaling higher level information is important. Furthermore, crop yields for most crops were below the national average of 1–2 tons for most for cereals and grain legumes. This reflects the higher production risks associated with crop production in semi-arid dryland areas.

#### *2.6.2.2 Localized livestock units with associated risk*

Results in **Table 4** indicate that cattle are the most significant livestock asset. Locally adjusted livestock units tended to be relatively smaller for cattle and sheep. There was some increase in the locally adjusted livestock units for goats and chickens. Given that the local livestock units were the denominator of the normalization factor, it means there were fewer goats and chicken but more cattle and sheep in the locales on average than in the rest of the respective districts. At the district scale, the numbers of goats and chickens were much variable over the period of 8 years. Locally adjusted livestock units tended to vary widely over the years, but with only limited differences across animal types. Goats and chickens are normally sold by agro-pastoralists for immediate cash needs, while decisions to sell cattle


**139**

**Table 5.**

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists…*

**Types Unadjusted livestock Locally adjusted livestock**

Cattle 2449339.20 852204.00 0.35 750101.20 1108561.00 1.48 Goats 200464.40 141244.40 0.7 258387.20 383990.00 1.49 Sheep 103268.40 43691.20 0.41 18273.60 22033.60 1.21 Chicken 71740.80 56381.20 0.76 127069.20 150926.40 1.19

**Mean Std. Dev. CV Mean Std. Dev. CV**

are carefully considered. Therefore, due to the volatility in the local production conditions and outputs, the aggregated information cannot be generalized for local

An efficient portfolio is either a portfolio that offers the highest expected return at a given level of risk, or one with the lowest level of risk for a given expected return. The majority of agro-pastoralists manage combinations of two crops and two livestock species. The paired combinations of crops and livestock were the basis for developing integrated portfolios through a permutation process. **Table 5** shows one crop-livestock portfolio (Sorghum, sunflower, cattle and goat) with highest returns at a less level of risk. As presented earlier, maize is the predominant enterprise. Maize-beans-cattle-sheep had the highest returns followed

**Portfolio Expected return (TShs) Risks (TShs) CV** Maize, beans, cattle and sheep 382937.20 206574.40 0.54 Maize, sorghum, cattle and chicken 442307.60 221783.60 0.5 Millet, beans, cattle and chicken 240376.80 140229.20 0.58 Maize, groundnut, cattle and chicken 330034.00 175310.00 0.53 Maize, cowpea, cattle and goat 283015.20 157092.80 0.56 Maize, sunflower, cattle and goat 283541.60 156077.60 0.55 Sorghum, sunflower, cattle and goat 1129880.00 486694.40 0.43 Maize, pigeon pea cattle and sheep 140962.40 130660.00 0.93 Maize, green gram, goat and sheep 108776.80 106370.40 0.98 Maize, millet, cattle and goat 386227.20 204694.40 0.53

Apparently, the high-return high-risk portfolio categories tended to include cattle and beans, which are high value commodities. However, these two commodities are sensitive to climatic shocks, especially droughts. Beans are the major source of food protein that are widely consumed and traded in both rural and urban areas of Tanzania. Beans from northern Tanzania are also exported to Kenya. Maize-green gram-goat-sheep and maize-millet-cattle-goat were the least-risk, lowest-return portfolios. Early maturing maize varieties, millet, and green gram are drought

*2.6.2.3 Potential diversification of crop and livestock portfolios*

*DOI: http://dx.doi.org/10.5772/intechopen.94133*

*Unadjusted and locally adjusted livestock revenue (Tshs).*

realities.

**Table 4.**

by millet-beans-cattle-chickens.

*Expected returns and risks of potential crop-livestock portfolios.*

#### **Table 3.**

*Unadjusted and locally adjusted revenue (Tshs).*

*Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists… DOI: http://dx.doi.org/10.5772/intechopen.94133*


**Table 4.**

*Agrometeorology*

*2.6.2 Crop and livestock production with associated risks*

quarter and a half percent over the aggregate district-level yields.

The district-level secondary data for seven growing seasons were localized through normalization with observed crop yields and livestock units in the survey localities. **Table 3** shows that, exception for millet, localized yields were higher than unadjusted aggregate district-level crop yields. For most crops, including sorghum, green gram, maize, groundnuts and beans, the localized yields ranged between a

The temporal volatility of locally adjusted crop yields, as reflected by the coefficients variation, was apparently higher than unadjusted district-level yields. Implicitly, local yields were highly variable with higher variances that were extended into adjusted aggregate yields through the normalization process. Although the aggregate data includes data collected outside the survey localities, it is still plausible to argue that the study locales represent the areas of respective districts. Therefore, in order to realistically reflect the local production risks faced by agro-pastoralists, downscaling higher level information is important. Furthermore, crop yields for most crops were below the national average of 1–2 tons for most for cereals and grain legumes. This reflects the higher production risks associated with

Results in **Table 4** indicate that cattle are the most significant livestock asset. Locally adjusted livestock units tended to be relatively smaller for cattle and sheep. There was some increase in the locally adjusted livestock units for goats and chickens. Given that the local livestock units were the denominator of the normalization factor, it means there were fewer goats and chicken but more cattle and sheep in the locales on average than in the rest of the respective districts. At the district scale, the numbers of goats and chickens were much variable over the period of 8 years. Locally adjusted livestock units tended to vary widely over the years, but with only limited differences across animal types. Goats and chickens are normally sold by agro-pastoralists for immediate cash needs, while decisions to sell cattle

**Crops Unadjusted district-level revenue (ton/ha) Locally adjusted revenue (ton/ha)**

Maize 265155.20 63638.00 0.24 375812.00 245828.80 0.65 Cowpeas 482332.80 352575.20 0.73 511172.00 507919.60 0.99 Beans 283974.00 89206.00 0.31 578570.00 466446.80 0.81 Sorghum 180968.80 36960.80 0.2 245302.40 136375.20 0.56 Green gram 201761.60 140492.40 0.7 335786.80 386753.60 1.15 Millets 245716.00 78527.60 0.32 235958.80 113006.80 0.48 Sunflower 179408.40 74166.00 0.41 229247.20 166135.60 0.72 Pigeon peas 70556.40 70255.60 1 125584.00 194655.20 1.55 Groundnuts 834024.40 564639.20 0.68 1395787.20 1583467.60 1.13

**Mean Std. Dev. CV Mean Std. Dev. CV**

*2.6.2.1 Localized crop yields with associated risks*

crop production in semi-arid dryland areas.

*2.6.2.2 Localized livestock units with associated risk*

**138**

**Table 3.**

*Unadjusted and locally adjusted revenue (Tshs).*

*Unadjusted and locally adjusted livestock revenue (Tshs).*

are carefully considered. Therefore, due to the volatility in the local production conditions and outputs, the aggregated information cannot be generalized for local realities.
