THE EFFECT OF SUSTAINABLE LAND MANAGEMENT TECHNOLOGIES ON FARMING HOUSEHOLD FOOD SECURITY IN KWARA STATE, NIGERIA

: Nigeria is among countries of the world confronted with the food insecurity problem. The agricultural production systems that produce food for the teeming population are not sustainable. Consequently, the use of Sustainable Land Management (SLM) technologies becomes a viable option. This study assessed the effect of SLM technologies on farming households’ food security in Kwara State, Nigeria. A random sampling technique was used to pick 200 farming households for this study. The analytical tools included descriptive statistics, Shriar index, Likert scale, food security index and logistic regression analysis. The results indicated that the average age of the respondents was 51.8 years. The food security index showed that the proportions of food secure and insecure households were 35% and 65% respectively. The binary logistic regression revealed that SLM technologies were one of the critical determinants of food security. An increase in the usage of SLM technologies by 0.106% raised food security by 1%. Other important factors that were estimated included farm income, family size, gender and age of the household head. To reduce the effects of food insecurity, the effective coping strategies adopted by the respondents were reduction in quantity and quality of food consumed, engaging in off-farm jobs to increase household income and using of money proposed for other purposes to buy foods. Governments at all levels should encourage the adoption and use of SLM technologies through both print and electronic media. Policies and strategies towards reducing the household size should be vigorously pursued to reduce food insecurity.


Introduction
Food is the key to life. It represents a large part of typical Nigerian household expenses. Thus, food security is critical to any country of the world. Food security occurs when all people, at all times, have physical, civic and financial means to provide adequate, safe and nourishing food that satisfies their dietary requirements and food choices for an energetic and beneficial life (FAO, 2005). Food insecure and secure households are those whose food intake falls below and above their minimum calorie requirements respectively.
In spite of the available resources and the efforts made by governments at different times, food insecurity remained one of the most significant challenges to Nigeria's economic development (Ifeoma and Agwu, 2014). The cost of food insecurity is substantially high. The poor performance of the agricultural sector deepens the food security problem of the country. Thus, it becomes more pertinent to increase the productivity of the sector. The agricultural sector is expected to create foods for the people. The agricultural production technologies and practices adopted to a greater extent determine whether a farmer will be food secure or not. Knowing the best technologies and practices to achieve this goal is significant (Branca et al., 2013). The disadvantages of the dominant model of agricultural intensification include the increased use of capital inputs and problems of economic feasibility (IAASTD, 2009). Consequently, concern is given to the alternative method of intensification such as the use of SLM technologies. SLM technologies refer to practices and technologies that relate to the management of land, water, biodiversity, and other resources to meet human needs without endangering the ecosystems. The adoption of SLM technologies can lead to improved soil texture and structure as well as it can raise the activity of soil flora and fauna (World Bank, 2006;Pretty, 2011). It can also make farmers less vulnerable to climatic risks. Many studies (Ahmed et al., 2016;Amaza et al., 2008;Omonona et al., 2007;Babatunde et al., 2007) have been carried out to investigate factors influencing food security of households. However, none of these studies have assessed the effect of SLM technologies on household food security. Thus, this study measured food security status, assessed the effect of SLM technologies on food security and described the reliable coping strategies used by the respondents to reduce the effect of food insecurity.

Area of study
The study area was Kwara state. The latitude and longitude of the state are: 8º and 10º north and 3º and 6º east respectively. The state has an area of 35,705 sq kilometers with a population of 193,392,500 people (NPC, 2016). To the west, Kwara state shares the international boundary with the Republic of Benin and to the north, the interstate boundaries with Niger state. It also shares boundaries with Oyo, Osun and Kogi states to the southwest, southeast and east respectively (Figure 1).
The climate consists of both wet and dry seasons each lasting for nearly six months. The raining season starts in April and ends in October while the dry season commences in November and stops in March. Temperatures range from 33°C to 34°C, with the total annual rainfall of about 1,318mm. The main occupation of the people is agriculture. The common crops grown are cassava, millet, maize, okra, sorghum, beniseed, cowpea, yam, sweet potatoes, and palm tree. The state has about 1,258 rural communities and the rural dwellers are the majority. Based on ecological characteristics, cultural practices and project administrative convenience, the state is categorized into four zones by Kwara state Agricultural Development Project (KWADP). These are:

Results and Discussion
Method of data collection and sampling Primary data were gathered using a structured interview schedule. A threestage random sampling procedure was adopted for this study. Two out of the four Shehu A. Salau et al. 206 ADP zones were randomly selected in the first stage. This was followed by a proportionate selection of 20 villages from the two selected zones. Lastly, ten farming households each were picked randomly from the chosen villages to make a total of 200 farming households as shown in Table 1. The state has about 185,000 farm families (KWADPs, 2010). Analytical framework The tool of analysis comprised: descriptive statistics, Likert scale, food security index and logistic regression. The socio-economic features as well as the effective critical strategies adopted by respondents were explained using descriptive statistics. The respondents were further grouped into food secure and food insecure households using food security index. The index is stated as follows: Fi = Per capita food expenditure for the i th household divided by 2/3 mean per capita food expenditure (MPCFE) of all households; where Fi = Food security index, when Fi > 1 = Household is food secure, and Fi < 1 = Household is food insecure. A situation where the per capita monthly food expenditure (PCMFE) of a household is larger or equal to two-thirds of MPCFE the household is food secure. On the other hand, a food insecure household is a situation where the PCMFE is smaller than two-thirds of MPCFE (Omonona et al., 2007). The proportion of food secure/insecure households was estimated using the headcount ratio (H) as follows: (1)

Headcount ratio ,
where M = Proportion of food secure/insecure households, N = Proportion of households in the sample.
To ascertain the effect of SLM technologies on household food security, a binary logistic regression model was employed.
The model is stated as: Z = m o + m 1 X 1 + m 2 X 2 + … + m k X k + u, where Z = Explained variable, The effect of sustainable land management technologies on farming household food security 207 mo = Constant, m 1 , m 2 ,…,m k = Coefficients, X = Explanatory variables, K = Number of explanatory factors, P = Probability, u = Error term. The explanatory factors are: X 1 = SLM technologies which were measured using Shriar index (2005), X 2 = Estimated farm income (N), X 3 = Number of years of schooling (years), X 4 = Household size (adult equivalent), X 5 = Co-operative membership; (COOP) (Yes=1; No=0 for COOP), X 6 = Sex of household head (D=1 for male; D=0 for female), X 7 = Age of the respondents (years). Table 2 shows the different SLM technologies, the scale ranges and their associated weights.  Table 2 shows that not all the farming activities could justify 0-3 scaling. From all the activities, the maximum attainable point was 46. The SLM index is given as:

Estimation of Shriar index
(3) where: SLM = Sustainable Land Management technology index for the i th household, S = Scale range for the activities employed by the i th household, and W = Weight of the activities used by the i th household. If a household is engaged in any activity, it gets point 1 and 0 otherwise. The scale range of 0-3 suggests that if the household is engaged in the activity and if so, it does so at low (1 point), medium (2 points), or high (3 points) scale. This classification was based on the percentage of the total area cultivated on which the strategy was employed. Production practices like the use of legumes are more endurable and so attracted the highest weighting of 3.5 (Salau et al., 2011). Intercropping with other crops besides legumes takes the value of 0, for no, and 1 (low), 2 (medium) and 3 (high) levels of activity respectively. The scale range of organic fertilizer application, water management, agroforestry and mulching starts from 0 to 1 -zero for no activity, and 1 if used. The scale of minimum tillage takes the value of 0 for no activity, and 1, 2 and 3 for the use of tractor, animal traction and hoes/cutlass respectively.
To identify the effective coping strategies, a three-point Likert scale was employed. The response options and values assigned were as follows: very effective = 3; effective = 2; and not effective = 1. These values were added and divided by 3 to obtain the mean (2.0). Strategies with mean scores greater and lower than 2.0 will be regarded as effective and not effective respectively.

Socioeconomic characteristics of respondents
The majority (94.5%) of the respondents were males. Based on the culture and tradition of the people, the male respondents usually had more access to farmland when compared with the female respondents. The mean age of the respondents was 51.8 years. This implies that most of the respondents were aged. Age is a critical variable which can affect the ability and agility with which the head meets the food needs of the household. An old household head is more likely to have a larger family size and may lack the energy required to work for the upkeep and sustenance of the family (Table 3).
About 35% of the household heads had access to credit facilities from cooperative societies. Access to credit facilities may affect the type of food eaten and expenses of households. A large (62.5%) proportion of the household heads were literate. Hence, the respondents are supposed to be able to take good decisions which will likely enhance their food security status (Babatunde et al., 2007). The respondents operated at a subsistence level with a mean farm size of 1.5 hectares. The size of farmland cultivated may affect production and food security of the respondents (Akinsanmi and Doppler, 2005). Furthermore, the study revealed that most (62.5%) respondents received between N50, 000 and N100, 000 monthly from agricultural and non-agricultural related jobs respectively.  Salau et al. 210 Food security status of farming households The calculated MPCFE was ₦4219.787. Households whose per capita food expenditure fell below and above ₦4219.787 were designated food insecure and food secure households respectively. Hence, 35% and 65% of the farming households were food secure and food insecure respectively (Table 4).

Factors influencing food security of households
The result indicated an R 2 value of 48.1%. This suggests that about 50% of the total variation in the explained variable was accounted for by the explanatory variables. Factors influencing food security were the adoption of SLM technologies, estimated farm income, family size, gender and age of the household head (Table 5). .030** Source: Field survey, 2018; *, **, *** significant at the 1%, 5% and 10% levels respectively.
The coefficient of SLM technologies used was positive and critical at the 1% level. This suggests that the adoption of SLM technologies was an important factor influencing food security in the study area. An increase in the usage of SLM technologies by 0.106% raised food security by 1%. The higher the percentage of SLM technologies adopted, the larger the chance of being food secure. Estimated income is also significant at the 1% level. This implies that the higher the income of the households, the more secure the household is. These findings agree with those of Amaza et al. (2008) and Ifeoma and Agwu (2014). Household size was negative and it was also important at the 1% level of probability. This suggests that larger households may be food insecure. This finding agrees with those of Tilksew and Beyene (2012) and Ifeoma and Agwu (2014). Age of respondents was important at the 5% level, but it had a negative relationship with food security. This indicates that the young respondents were more food secure when compared with the aged ones. An old household head was more likely to have larger household size and may lack the energy required to work for the upkeep and sustenance of the households. Sex of the household head was also negative and important at the 5% level of probability. This suggests that female-headed households may be more food secure than their male counterparts. Surprisingly, education and cooperative participation were not the factors that influenced food security in the area.

Coping strategies employed by households
The most effective coping strategies adopted by respondents to reduce food insecurity included: reduction in quality of food eaten (M=2.06), consuming less preferred foods (M=2.09), using money budgeted for other uses to purchase foods (M= 2.14), doing off-farm jobs to raise income (M=2.12) ( Table 6). This finding agrees with the results of Haile et al. (2005), who have opined that engaging in off-farm and non-farm jobs is necessary for diversification of household income. Other strategies are borrowing food from friends and relatives (M=1.76), borrowing money to purchase food (M=1.81), purchasing food on credit (M=1.72), and lowering the number of people eating in the household (M=1.40). According to Ifeoma and Agwu (2014), household assets could be disposed to purchase food in times of adversity, crop failure and other eventualities.

Conclusion
This study assessed the influence of SLM technologies on household food security in Kwara state, Nigeria. The study indicated that 35% and 65% of the respondents were food secure and food insecure respectively, with an average age of 51.8 years. Furthermore, the adoption of SLM technologies was found to be significant in explaining food security of households in the state. An increase in the usage of SLM technologies by 0.106% increased food security by 1%. Other important determinants estimated were farm income, household size, gender and age of the household head. Moreover, reduction in quality of food consumed, engaging in off-farm jobs to raise income and diversion of funds budgeted for other uses to purchase foods were some of the effective coping strategies used by the respondents in reducing the effects of food insecurity. Consequently, it is recommended that the adoption and use of SLM technologies should be encouraged at local, state and federal levels by sensitizing farmers on the significance of SLM technologies through print and electronic media. Policies and strategies aimed at reducing household size should be formulated and implemented to reduce food insecurity.