Poverty Status and Dietary Diversity Among Farming Households in Gassol, Taraba State, Nigeria

This study examined the relationship between poverty status and dietary diversity among farming household in Gassol, Taraba State, Nigeria. Simple random sampling technique was employed to select 120 households in the study area from whom data were collected. The data were analyzed using Household Dietary Diversity Score (HDDS); Foster, Greer and Thorbecke (FGT) Index; and Tobit regression model. The result of HDDS showed that 92% of the farming households had low dietary diversity while the FGT result showed that 70% of the farming households were poor. Tobit regression revealed that the coefficients of household size, farm size, household dietary diversity and income from sales of non-agricultural goods were negatively significant; however gender and marital status were positively significant. Based on this, it was established that there is a significant relationship between poverty status and HDDS. This relationship was such that the higher the level of poverty, the lower the dietary diversity of the farming households. This implies that the poorer a farming household is, the lesser their dietary diversity and nutritional intake. This could be attributed to the minimal profit margin obtain by farmers owing to the fact that they mostly sell their farm produce immediately after harvest. Consequent upon this, they remain impoverished and unable to feed well. It is therefore recommended that the shelve lives of these agricultural produce should be enhanced through research. There is also need for the farmers to be educated on the importance of balance diet. Finally, they should be supported with nutritional supplements to improve their health status and consequently their poverty status.

plays its optimum role in human health. Dietary diversity has been positively linked with these three pillars of food security (Hoddinnott and Yohannes, 2002). Individual and household access to food has also been shown to be affected by demographic and socioeconomic factors, accounting for variations in diet quality (Kwaghe, 2006). Studies have demonstrated the contribution of dietary diversity to population-level nutrient adequacy in developing countries, with fewer studies considering the value of cultivated and wild biodiversity (Ruel, 2003). It has recently been shown that in a peri-urban area of Dakar, Senegal (in west Africa), dietary diversity is positively correlated with intakes of several key nutrients, specifically Ca, Fe, Zn, vitamin A, vitamin C, thiamin, riboflavin and vitamin B6 (Kennedy, et. al. 2009). The study in Vietnam, which included adult women, validated the diversity measures against nutrient intake and nutrient density. The findings confirm a positive association between the two measures of diversity and intake of energy and a variety of nutrients. Using a multi -country analysis of data from 10 countries Hoddinott and Yohannes (2002) tested whether household dietary diversity was associated with consumption/expenditure and food security. The results indicate that as income increases people tend to diversify their diet (Torheim et al., 2004). Diversity also significantly improves dietary quality and the likelihood that individuals will meet their daily nutrient requirements, especially with regard to essential micronutrients and it may be a good proxy for greater income/expenditure and food security (Heady and Oliver, 2013).
This paper follows the conventional view of poverty as a result of insufficient income for securing basic foods with the resultant effects as malnutrition, sickness and diseases. The emphasis here is with the individual's ability to subsist and to procreate as well as to command resources to achieve a living (Sen, 2004 andAmins and Rakodi, 1994). Historically this involves a transition from a situation where subsistence depends upon wages with which to purchase foods.

3.1Study Area
Gassol is a Local Government Area in Taraba state, Nigeria. It's headquarter is in the town of Mutum Biyu on the A4 highway. It is located between latitude 8 0 24' and 8 0 40'N and longitudes 10 0 32' and 10 0 53'E. It has an area of about 5,548km 2 . Based on a population growth rate of 3 percent, the estimated projected population stands at 328,922 (2006 census projected to 2016). The northern border of Gassol is the Benue River, the Taraba River flows north through the area and empties into the Benue. Gassol is one of the eight LGA's of Taraba state whose majority population is the mumuye people. The major crops grown are: Yam, cassava, maize, guinea corn, cowpea, egusi and groundnut.

Sampling Technique and Procedure.
Farming household constituted the population of this study. There are twelve (12) major villages in the study area (Mutum Biyu A, Mutum Biyu B, Sabongida, Sendirde, Shira, Namnai, Yerima, Tutare, Gassol, Wuryo, Gunduma and Wurojam). Six (6) villages were randomly selected using balloting techniques. In each village a proportionate number (10%) of respondents were selected from the sample frame. Questionnaires were administered to households in the ratio as follows: total of 25 questionnaires were administered in Gassol,25 in Mutum Biyu A,20 in Gunduma,20 in Shira,15 in Yerima,and 15 in Tutare making the total of one hundred and twenty (120) questionnaires. The distribution was informed by the number of active farming households in the villages.

Data Analysis.
Primary data was used for this study. Primary data was sourced from a set of well-structured research questionnaire in line with the stated objectives of this work which was administered to the selected households in the study area. Data generated was analyzed using Household Dietary Diversity Score (HDDS); Foster, Greer and Thorbecke (FGT) Index, and Tobit Regression.

3.3.1Household Dietary Diversity Score (HDDS)
Data on household dietary diversity was collected using 24 hour dietary intake. The information collected on dietary consumption was used to calculate a dietary diversity score (DDS), define as the number of different food groups consumed by family members over 24 hours. Dietary data was collected by means of a validated 24 hours recall which is not quantified. Respondents were visited in their homes during the survey. Both spouses constituted the respondents. The women were involved because they are mostly in charge of cooking meals for households, it is assumed that they have good ability to remember foods eaten (Haggblade, et .al., 2007). For this study the twelve (12) food groups recommended by Food and Agriculture Organization of the UN (FAO, 2007)  11. Sugar / honey 6. Eggs.

Foster Greer Thorbecke (FGT) Index.
FGT was used to assess the poverty status of the respondents. The FGT consider poverty as dependent on the poverty gap ratio, and assume as the power of that ratio, thus; Incidence of poverty = Where; Yi = the average consumption per capita for the 1 st household When household are ranked in ascending order of consumption. Z = the poverty line.
[Z-Yi] = the poverty gap household 1. n = the total number of the household below poverty line. q = the FGT and it takes values as 0, 1,2.

The Tobit Regression
The Tobit model is expressed following Tobin (1958). Tobit decomposition framework examined the relationship between poverty status and the socio -economic factors that influenced poverty among the respondents. The Tobit Model can be mathematically expressed as:  (12) food groups included in the (Household Dietary Diversity Score) were cereals, roots and tubers, legumes and products, meat and product; fish and sea foods; dairy and products, fruits and product, bakery product; fat and oils, poultry and eggs and miscellaneous. These food groups were used to identify food intake quality of households in the study area. Dietary diversity indexes have been shown to be good proxy for calorie intake and nutritional outcomes (Ruel, 2006 Table 1 shows that 92% of the sampled household had 0-6 food groups in their diet per day, while 8% of the households had 7-12 food groups in their diet. This implies that most households had low food intake diversity per day due to competing demand on available household income which limits their access to varieties of foods. Also, this result was not unexpected because some of the households do not produce significant share of their own food. Hence they were more exposed to rising food prices which influences their level of food diversity. Household access to food was therefore an important variable in food diversity level in the study area.

Poverty profile
The respondents were classified into an exclusive group separated by the line either as poor or non -poor .The poverty line use for this study was calculated from the yearly household expenditure (THE) of sample household. Two third (N52,812) of the yearly THE of the sample households was use as the poverty line. The poverty status category of households included: Poverty head count or incidence ( ), poverty gap or depth ( ) and squared poverty gap or security ( ).
The for the household was 0.7. This means that 70% of the farming households in the study area were poor. The poverty gap index was 0.415, this implies that 41.5% (N21, 916.98) of the poverty line was required to bring an average poor individual in the study area to the poverty line. The Poverty index which measures the distance of each poor person to one another was found to be 0.219, showing that 21.9% of the poor households were severely poor. This indicated inequality in the degree of poverty among poor households.

Relationship between Poverty Status and Household Dietary Diversity.
The Tobit regression model was used to determine the relationship between poverty status and the socioeconomic factors that influenced poverty among the respondents in the study area. It measured the parameters of the conditional probability of being poor and the marginal changes in explanatory variables on the poverty status of the respondents. Respondents were classified into poor and non-poor using the poverty line as derived from The Household Expenditure (THE). The regression parameters and diagnostic statistics were estimated using the maximum likelihood estimation (MLE) technique.
Results showed that only six out of the twelve listed regressors had significant influence on the poverty status of the farmers. The Log likelihood function is negative (-52.673) while the Chi-square value is positive and significant. This implies that there is a relationship between poverty status and dietary diversity in the study area. The variables that had significant co-efficients are gender, marital status, household size, farm size, HDDS and income from non-agricultural products. It should be noted that a positive sign on a parameter indicated that higher values of the variable tend to increase the likelihood of being poor. Similarly, a negative value of a co-efficient implied that higher values of the variable would decrease the probability of being poor; all things being poor.
The co-efficient of gender of the household head is 0.339. This implies that relative to the female-headed households, the level of poverty will be reduced by 0.339 for male-headed households, hence having a poverty depth of 0.076 as against 0.415 for female-headed households. This could be attributed to the involvement of maleheaded household in different forms of off-farm activities. The co-efficient of marital status of household head is 0.099, implying that the poverty status of household headed by married people will be increased by 9.9% to become 51.4%, while that of households headed by un-married people will remain as 41.5%. The reason for this is married Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.11, No.2, 2020 households tend to have larger household size, which raises the dependency ratio.
Household size was found to have a negative coefficient and significant at 10% level. This means that the larger the household the lower the poverty status, an increase in the household size will probably lead to a reduction in poverty status of the households. Farm size of the households was found to have a negative coefficient and significant at 10% level. This means that farm size is inversely proportional to poverty status that is, an increase in farm size will probably lead to a reduction in poverty status of the households. Non-agricultural income was also found to have negative coefficient and significant at 10% level. The availability of additional income beside agriculture will reduce poverty status of the households. That is, an increase in non-agricultural income will probably lead to a reduction in poverty status.
The Dietary Diversity Score of farming household, had a negative coefficient and significant at 5% level, meaning that an increased in the varieties of foods consumed by households, could lead to a reduction in poverty. Thus, the poorer the farming household is, the lower their dietary diversity score. The effect of dietary diversity on the household might result in low productivity and therefore poverty. This is because the lower the dietary diversity of a farming household the lower their nutritional intake and this might subsequently affect health of the household members which will reduce farming productivity, and thus increase the poverty status of that household (Hoddinott et, al. 2006). Note: * and ** means significant at 10% and 5% levels of probability.

Conclusion
Studies have shown a direct relationship between food insecurity, hunger and poverty. One of the contributing factors to food insecurity is socio-economic status. Limited income causes people to restrict the number and quality of meals they eat, reduce dietary variety, and look for inexpensively processed food. These options are usually low in essential nutrients and high in fats with empty calories.
It is reported that Nigeria's total agricultural output in areas of food production (including livestock and fishing), processing and marketing accounted for about 80% by value. However, in spite of the increase of food to the Nigerian agricultural economy, the food intake in the country is still inadequate in terms of quantity and quality. Food consumption studies assess immediate causes of malnutrition, and food security studies predict the adequacy of household dietary intake and nutritional status. Widespread poverty resulting in chronic and persistent hunger is the biggest problem in the developing countries. In Nigeria, malnutrition is associated to food shortage linked to both quantity and quality of food to provide a balance diet.
The frequency distribution of household dietary diversity index presented in this study shows that 92% of the sampled household had 0-6 food groups in their diet per day, while 8% of the households had 7-12 food groups in their diet. This implies that most households had low food intake diversity per day due to competing demand on available household income which limits their access to varieties of foods. The study found that there is significant relationship between poverty status and dietary diversity. This relationship was found to show that the higher the level of poverty, the lower the dietary diversity of the farming households. Thus, the poorer the farming household is, the lesser their dietary and nutritional intake. To ameliorate poverty and inadvertently improve their dietary and nutritional intake, it is recommended that: a. The capacity of farming households be built to take farming as a business through sensible