Factors Influencing Land Utilization Among Poor Rural Household in Kedah, Malaysia

Target Participants

The study was conducted to determine factors that influence utilization of ecosystem services, especially in provisioning services by the poor communities. Initially, the researchers conducted the study rural communities. About 179 households were registered in e-Kasih with a poverty rate of more than 1.0 (Ministry of Women, Family and Community Development, 2014).

Data Analysis

Questionnaire data were processed, coded and analyzed using the Statistical Package for Social Sciences (SPSS) software version 22. Descriptive statistics (percentage, mean, and frequency count) were used to characterize socioeconomic variables such as gender, age, and income. Multiple regression was used to determine the factors of influence land utilization among poor rural households and analyze the relationships between land utilization and yield. Seven variables will be analyzed in multiple regression are social interaction, level of technology, infrastructure, transportation, market accessing, season, and climate change refer on Table 01 . The model of multiple regression (Bakar, 2007) used is specified as:

Data Collection

Data were collected detailed from the household head survey field. A household head was used as the basic unit of the survey. In this regard, a household was defined as a group of people living together, making common arrangements for food and other essentials for a living (Kamwi et al., 2018). A questionnaire survey was adopted, and 124 household heads were selected based on snowball sampling in sub-district Jerlun, Kedah. The households selected are low-income which identified as household earning less than RM1200 per month (without government assistance). The questionnaire survey mainly focused on socio-demographic information of household head and their livelihood activities based on land resources, and factors of influence land utilization among poor households in the study area.

Study Area

The study was conducted in sub-district Jerlun, Kubang Pasu District (refer Figure 01 ). According to the Kubang Pasu District Council (2016), there are 167 villages in Jerlun Sub-district with a population of 53,383 people (of which Malay ninety per cent, Chinese eight per cent and others two per cent). Besides, the availability of natural resources, especially land agriculture meets the criteria of the study.

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Significant Factors influence land utilization among Poor rural Household

Based on the multiple regression analysis, the results show that the R correlation coefficient of the dependent variable with the independent variable is 0.559 which shows a good relationship as the value is greater than 0.50. Accordingly, the R² value of 0.582 indicates that as much as 58.2% of the variation in land utilization can be explained in this analysis. While the remaining 42.8% of the land resource use is influenced by other factors not discussed in this study. Besides, the Adjusted R² value explains how well the model can be generalized to the population, so ideally the closer the Adjusted R² value to R² is. The adjusted value of R² from the research findings is 0.579 or 57.9%. The difference between R² and Adjusted R² values ​​is 0.003 or 0.3%. The standard error of the Estimate (SEE) is 0.363, where the smaller the SEE value is, the better the model will be in predicting the dependent variables.

Besides that, researchers can also identify the important and powerful independent variables influencing the land utilization in their settlements in Table 03 . The strength level of a variable will be expressed by the standardized value of β. Therefore, it can be said that the most influential variables affecting land utilization are factors of social interaction (the type of social interaction) with a value of β = 0.443 followed by a climate change (yield) factor of using β = 0.424 and a type of transportation factor having a value of β = -0.294. This is because this type of social interaction can help households perform various agricultural activities in obtaining agricultural produce. Generally, the interactions that occur are informal social interactions that are co-sponsored by family members, while formal interactions are those involving governmental organizations such as subsidizing the population engaged in agricultural activities. As Siwar & Chamuri (2006) have argued, social interaction plays an important role in utilizing and managing natural resources among the poor community. Similarly, Ledger (2009), Omar (2016), Umunnakwe (2014) and Xu et al. (2015) have found that social interaction is a prerequisite for assistance or information on more productive and effective management of land resources.

Climate change factors, in turn, are an important second factor influencing land utilization. Climate change is a natural or indirect phenomenon that causes the human activity to change or adapt to these conditions (Phuang, 2011) in particular activities. As in the study of Siwar et al. (2009) have found that most farmers adapt to climate change only on the basis of common actions. This, in turn, results from the impact of agricultural activities such as rice farming, vegetable crops and fruits. Also, statements from the results of the informal interviews of respondents also indicate that climate change also affects land use among the poor.

The physical factor of the type of transport represents the third and most important relationship in influencing the poor to use their land resources. This is because, transport is the heart of man to move from one place to another (Amin & Manap, 2015). National Economic Consultative Council Report (2001) has proven that transportation is an important element of poor families' lives. Here, the importance of transport to the use of land resources is the transportation of agricultural produce and the movement of the source of the land. According to Shafie and Mahmud (2015) with the availability of transportation that can increase the degree of arrivals to a destination and save on cost and travel time. Accordingly, the regression equation can be generated as in Equation 01:

$$Y=8.440+0.531X1+(-0.395X10)+0.483X18\dots Equation01$$

The relationships between land utilization and yield

Based on the Table 04 , the results show that the correlation coefficient R for the dependent variable with the independent variable is 0.649 which shows good relationship as the value is greater than 0.50. Accordingly, the R² value of 0.602 indicates that up to 60.2% of the variation in natural resource use factors influenced the land resource benefits that can be described in this analysis. While the remaining 39.9% of the land source income derived from households is influenced by other factors not discussed in this study. Besides, the Adjusted R² value explains how well the model can be generalized to the population, so ideally the closer the Adjusted R² value to R². Adjusted R² value of the research findings was 0.591 or 59.1%. The difference between the value of R² and adjusted R² is 0.011 or 1.1%. The standard error of the Estimate (SEE) is 0.797, where the smaller the SEE value is, the better the model will be in predicting the dependent variables.

According to Table 05 , it was shown that there were only five variables with significant p values ​​<0.05, namely variable types of social interaction and social interaction form, type of infrastructure facilities, size of transportation and climate change. Besides, the t-test conducted by the researcher was to measure the significance of each independent variable coefficient on the dependent variable. In addition, researchers are also able to identify important and powerful independent variables in influencing the use of natural resources in their settlements. The strength level of a variable will be expressed by the standardized value of β.

Therefore, it can be said that the strongest variables related to land resource use are the types and forms of social interaction that have values ​​β = −0.676 and 0.468. Variable types of social interactions show inverse relationships with the benefits derived from land use. This is because, the type of interaction refers to one person’s interaction with another person. So, this shows that, in the use of land resources, the poor do not need to interact with many people as they will provide little if they are evenly distributed. Therefore, the results indicate a negative relationship. Varying forms of social interaction, however, show a direct relationship to the benefits that poor people derive from land use. This is because the forms of social interaction that are often involved are formal interactions with government and private entities and informal interactions that help to improve outcomes, especially in the agricultural sector such as subsidy assistance, labor assistance and so on. Thus, the results indicate a direct relationship between social interaction factors and the benefits of land resources. Next, it is followed by the variable type of infrastructure facility β = 0.316 that is the third most important factor affecting the benefit of land resource use. The relationship between these types of infrastructure facilities is positive in that they are directly proportional to the benefits that the poor benefit from the use of land resources. The type of infrastructure that is important to the poor in the use of land resources is a way of facilitating their movement of land resources to profit.

Transport also plays an important role in influencing land-use outcomes. This study shows that the size of the transport has a positive relationship that the larger the transport is then the more people can bring the result is β = 0.310. Based on field observations, most of the crops that require large-scale transportation are mainly rice and local fruits such as mangoes, rambutans, and bananas. While the climate change enabler recorded a value of β = 0.260, it also showed a positive relationship. In other words, climate change has a direct bearing on the use of land resources especially in crops. Climate change, such as prolonged heat, will cause plants or plants to die from the lack of water in the soil and therefore the yield for the poor will be affected. So climate change is also affecting the use of land resources among the poor. A summary of important and powerful independent variables influencing land resource use can be found in Table 5 . Accordingly, the regression equation can be generated as in Equation 02;

$$Y=1.765+(-1.126X1)+0.769X3+0.541X8+0.689X11+0.450X17\dots Equation02$$