Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia

doi:10.21203/rs.3.rs-3121512/v1

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Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia

https://doi.org/10.21203/rs.3.rs-3121512/v1

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published 07 Nov, 2023

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A variety of socioeconomic and environmental factors have contributed to changes in LULC around the world in recent years. This study examines the socioeconomic factors that accelerated LULC in Western, Ethiopia. Data was generated from landsat images and through both primary and secondary sources. Primary data include household survey, field observation, group discussion; key informants’ and interviews. Landsat images classified with supervised classification technique and maximum likelihood classifier through arc GIS 10.3 to develop LULC maps of the study area. Accuracy assessment and kappa coefficient were used to approve the accuracy of the classified LULC, and farm land, settlement, bare land, forest land, and water body were the major LULC classes in the District. Forest cover in three decades (1990–2020) decreased from 12.1% in 1990 to 2.6% in 2020 in the study area. Binary logistic regression model examined the relationship between the (dependent) and the main socioeconomic (independent) variables. A logistic regression was performed to ascertain how independent variables and the driving forces for LULC change (Natural forces or anthropogenic forces) and the model was statistically significant (x² = 23.971, df = 5, P < 0.001).The model explained 13.9% (Nagelekerke R²) of the variance in the driving forces for LULC dynamics and correctly classified 66.1% of cases. The study identified Age, Gender, Educational status and landholding sizes significantly determine driving forces for LULC dynamics and have the greatest chance to choice the anthropogenic forces. Thus relevant stakeholders should take integrated actions to decrease the driving forces for LULC dynamics through restoration of landscape.

Driving forces

logistic regression

socioeconomic

western Ethiopia

The relationship between people and nature has been explained and conceptualized by multiple ways throughout human history, and considerable heterogeneity still exists between cultures (Mace, 2014;Capaldi et al., 2014, Yeung & Yeung, 2005;Kansteiner, 2002). The human-nature interaction has recently led to LULC changes around the world (Berihun et al., 2021;Kidane et al., 2019). LULC change is a dynamic and complex process that can be caused by many interacting processes ranging from various natural to socioeconomic factors (Yesuph & Dagnew, 2019, Egarter et al., 2016). Drivers accelerating LULC changes may have positive or negative influences on the planetary system ( Roy et al., 2022; F. Wang et al., 2021Duraisamy, 2018 ). LUCC have been influenced by a variety of essential human endowments and biophysical phenomena (Li et al., 2016;Inc, 2005). The relationship between LULC change and its driving factors is complicated and dynamic as some of the previous studies suggest that demographic changes contribute more than any other causative factors of LULC changes (Bufebo & Elias, 2021, Belayneh et al., 2020,Jain et al., 2016).

In less developed countries like, Africa most population are engaged in agricultural activities and woody biomass extraction as a source of income leads to LULC changes (Wang et al., 2022;Sedano et al., 2022). In Ethiopia agricultural expansion into the forest land, logging, charcoal production and firewood harvesting have been the major drivers of LULC change (Negassa et al., 2020;Tesfaye et al., 2014). The drivers of LULC dynamics are diverse and complex varying in space and time, demanding more investigation in Ethiopia (Reid et al., 2000; Hassen & Assen, 2017; Tolessa, 2020). Agricultural land expansion, increasing demand of fuel-wood and construction material, illegal settlement within forests, illegal logging were/are said to be the major LULC change drivers in Ethiopia (Meragiaw et al., 2022; Kuma et al., 2022). In Western Ethiopia, anthropogenic activities-induced LULC changes are frequently observed in Wayu-Tuka District, where this study was conducted (Badasa et al., 2022; Urgesa et al., 2016). A recent study conducted by Negassa et al., (2020) in the vicinity of the present study area showed agricultural land expansion as one of the major drivers of LULC change in the area.

However, the focus of the LULC change detection made by Negassa et al. (2020) focused on forested LULC change. Thus, this study analyzed the socio-economic drivers of LULC change in the study area. Understanding these issues requires rigorous investigation about the ongoing LULC changes in the study. The study results are useful to formulate sound policies and management options towards the sustainable use and management natural resources in the study area (Badasa et al., 2022; Galata, 2020; Urgesa et al., 2016). Therefore, this study was aimed to analyze of the socio-economic drivers of LULC dynamics of Wayu-Tuka District in East Wallaga Zone, Western Ethiopia.

2.1. Description of the Study Area

The study area, Wayu Tuka District, is located in the Oromia National Regional State of East Wallaga Administrative Zone, about 322 km west of Addis Ababa. The district borders with Sibu Sire district in the North and East, Leka Dulacha district in the South, and Guto Gida in the West. Specifically, the study area is situated at 8^o 51’ 30” – 9^o 10’ 30” N latitude and 36^o 32’ 0” – 36^o 50’ 0”E longitude (Fig. 1). In the district there are mountain peaks and hill slopes like Komto, Gara-achani and Tuka which rises to elevations of about 3350, 3140 and 2350 m.a.s.l. respectively. The district possesses a total area of 54,590.4 ha and comprises 12 Kebeles (i.e., Kebele - is the smallest administrative unit in Ethiopia), 10 rural villages and 2 urban centers.

The zone had three agro-ecological divisions of which 11% is high land, 49% mid-land and 40% low lands (EWARDO, 2009). The zone receives mean annual rainfall of between 1000 and 2551.4mm, and on the other hand, the study area experiences about 2158mm annual rainfall. The recorded climatic data shows that the annual amount of rainfall of the study area was about 2525.3mm, 2551.4mm in the year 1993 and 1998 respectively (Fig. 2). The recorded climatic data shows that the annual amount of the area experienced the average minimum and maximum temperature of about12.5⁰c and 25.5⁰c (NMSA, 2014).

The main vegetation types observed in the study area are Afromontane upland forest, mid upland semi-deciduous forest, a few different forms of riverine forest, and plantation types. Southwest Ethiopia is known to have this sort of moist evergreen montane forest, which is what the local vegetation is (Assefa et al., 2020)

Soils of the study area are deep and belong to the orders Oxisols and Ultisols; of these, the major soil types and their spatial coverage in the district are clay loom 17,371.68 ha, sandy 10,133.49 ha and clay 1,447.64 ha which are suitable for agriculture such as cereal crops: maize, sorghum, and teff production in the district (WARDO, 2017).

Subsistence farming is the main economic activities in the study area-crop framing and livestock rearing. Based on the 2007 population and housing census, the population of Wayu Tuka District is projected to be 66,194 (Ethiopian census, 20015). Of 66,194, 32,391 were men and 33,803 were women.

2.2. Materials and Methods

2.2.1. Sources and method of data collection

The required data for the study were generated through primary and secondary sources. Primary sources of data are household survey, field observation, group discussion, key informants’ interview and remotely sensed data interpretation. Secondary data were mainly reviewed from relevant literatures from both published and unpublished materials.

2.2.2. Household Survey data

Household’s questionnaire survey carried out in the three villages namely Dalo komto, Gara Hudha and Kich villages that were selected based on proximity to forest area (Geographical location). Formula used to determine the sample size formula of the study. Sample size determination correction formula had been used to calculate the final sample size which is often used in most social science researches for sample size determination when the targeted population is less than 10,000. The sample size formula (Ajay & Micah, 2014):

no $=\frac{{{t}}^{2{*}}\left({p}\right)\left({q}\right)}{{{d}}^{2}}\frac{ {\left(1.96\right)}^{2}\left(0.5\right)\left(0.5\right)}{({0.5)}^{2}}$=0.38416x100~ 384

The total population of targeted villages were (1340 households). Therefore, for a population of 1340, the required sample size is 384. However, since this sample size exceeds 5% of the population (520*.05 = 26), (Naing et al., 2022) correction formula should be used to calculate the final sample size. These calculations are as follows

nl= $\frac{{n}{o}}{(1+\frac{{n}{o}}{{p}{o}{p}{u}{l}{a}{t}{i}{o}{n}})}$ n 1= $\frac{384}{(1+\frac{384}{520}}$ =221. Therefore the final sample size is 221.

Where:

no = desired sample size when population greater than 10,000;

n = sample size

n1 = finite population correction factors when population less than 10,000;

t = standard normal deviation (1.96 for 95% confidence level);

Where t = value for selected alpha level of .025 in each tail = 1.96 (the alpha level of .05 indicates the level of risk the researcher is willing to take that true margin of error may exceed the acceptable margin of error).

Where (p) (q) = estimate of variance = 0.25

Following the above sample size determination formula, a total of 221 housing hold units were randomly selected from three villages; 79 from Dalo Komto, 63 from Gara Hudha and finally 79 from Kich (Table 1).

Table 1

selected Households in the Study area
Villages	Total HHs	Sample size
Dalo Komto	175	Number	Percent
Dalo Komto	175	79	35.7
Gara Hudha	170	63	28.6
Kich	175	79	35.7
Total	520	221	100

The study district was purposively selected because of the ongoing LULC changes in the area. Similarly, sample/study villages were selected using purposive sampling method based on the presence of the forest in the vicinity. Accordingly, out of ten villages in the district, three villages namely Dalo komto, Gara Hudha and Kich forests were selected.

2.2.3. LULC change data

Data used for LULC changes of Wayu-Tuka District were obtained from satellite images taken in MSS of 1990, TM of 2000, ETM + of 2010, and OLI of 2020. These images were downloaded from USGS: http://earthexplore.usgs.gov. LULC change classes such as bare land, settlement, farmland, forest, and water body change information were obtained from landsat images.

2.2.4. Method of data Analysis

The driving forces of LULC of the independent (explanatory) variables on the dependent (response) variable: The driving forces of LULC were analyzed from the data which was generated through both primary and secondary sources. Both quantitative and qualitative data were analyzed during this study. The quantitative data obtained through the household survey were coded and entered into the statistical software version 20 (SPSS 20) for descriptive statistical analysis. The results were summarized and presented as percentages, tables and figures. These were used to determine and quantify the drivers’ of LULC changes in the study area.

Simple frequency analyses were used to describe socioeconomic characteristics of households and to review their responses and rankings of drivers of the land use/land cover changes (Mariye et al., 2022). The driving forces of LULC dynamics were analyzed using non-parametric test, specifically Pearson’s Chi-square Test (Alqadhi et al., 2021). Furthermore, a quantitative binary logistic regression analysis were employed at household level to identify the main socio-economic determinants of driving forces of LULC for the observed changes (F. Wang et al., 2021). Logistic regression analysis is a suitable statistical procedure to examine the relationship between the (dependent) and the various socioeconomic (independent) variables (Das & Pradhan, 2007). It is an effective technique for the analyses when the dependent variable is binary (Lesschen et al., 2005) which estimated

$${L}{o}{g}{i}{t}\left({Y}\right)={\alpha }+{{\beta }}_{1}{{x}}_{1}+{{\beta }}_{2}{{x}}_{2+ }{{\beta }}_{3}{{x}}_{3}\dots +{{\beta }}_{{n}}{{x}}_{{n}}$$

where Y = the dependent variable indicating the probability that Y = 1, α = the intercept, β₁……β_n = coefficients of the associated independent variables, and X₁…X_n = the independent variables.

In this study, the outcome variable was retreated against selected explanatory variables: age, gender, educational status and Land holding size were used as a proxy for socio-economic variables.

2.2.5. Results of Accuracy Assessment

Assessing accuracy of LULC classes showed the great values from the accuracy assessment are useful to considerate the level of the results obtained the landsat images depending on the classification of the LULC types of the study area in the period 1990,2000,2010 and 2020 independently. The aim of accuracy assessment was to quantitatively assess how effectively the pixel was sampled into the correct LULC classes. Furthermore, the key emphasis for accuracy assessment pixel selection was on areas that could be clearly identified on landsat high resolution image, Google earth and Google map. Therefore, accuracy assessment of the classified images plays a great role in evaluating reliability of the extracted information from the classification (Rwanga & Ndambuki, 2017).

For these images user’s accuracy, producer’s accuracy, overall accuracy and kappa coefficient were assessed for each image of LULC classes in the four periods i.e.,1990, 2000, 2010, and 2020 (Table 3). The producer’s accuracy and user’s accuracy were calculated so as to check the accuracy of LULC classification types.

As it is shows in Table 3, the estimated accuracy values showed that differences among the five LULC classes within the same period, and the accuracy values of each LULC classes also showed differences from period to period in the three decades considered in the study (1990–2020). For example, concerning the 1990 LULC classes of Wayu-Tuka District, the calculated levels of producer’s accuracy 72.7% and user’s accuracy 100% of farm land, the user’s accuracy 67.7% and the producer’s accuracy 66.7% of forest, the UA ( 67.7%) of settlement and the PA (72.7%) of farm land were found to be somewhat low. But, for the year 2000 the levels of water body UA (66.6%) and settlement (75%) were also relatively low. The UA level of settlement (62.5%) for the year 2010 was found to be the smallest all of the values from the five LULC classified in all four periods studied. The maximum accuracy level of all the five LULC classes was 100% (Table 3).

The overall accuracy levels of the LULC classes in all the four periods were 83.3%% for 1990, 86.6% for 2000, 80% for 2010 and 83.3% for 2020 (Table 2) and above, which satisfied the minimum requirement for accuracy level of the landsat images based on the classified LULC classes of the study area ( Obsa et al., 2021;Gong et al., 2017).

Table 2

accuracy assessment for 1990, 2000, 2010 and 2020
Accuracy assessment for 1990
LULC Classes	Bare land	Farm land	Forest	Settlement	Water body		Total User	User’s accuracy (%)
Bare land	4	0	0	0	0		4	100
Farm land	0	8	0	0	0		8	100
Forest	0	2	4	0	0		6	67.7
Settlement	0	1	2	6	0		9	67.7
Water body	0	0	0	0	3		3	100
Total producer	4	11	6	6	3		30
Producer accuracy (%)	100	72.7	66.7	100	100
Accuracy assessment for 2000
Bare land	4	0	0	0	0		4	100
Farm land	0	7	0	1	0		8	87.5
Forest	0	0	7	0	0		7	100
Settlement	0	1	1	6	0		8	75
Water body	1	0	0	0	2		3	66.6
Total producer	5	8	8	7	2		30
Producer accuracy (%)	80	87.5	87.5	85.7	100
Accuracy assessment for 2010
Bare land	4	0	0	0	0	4		100
Farm land	0	6	1	0	0	7		85.7
Forest	1	1	6	0	1	8		75
Settlement	1	1	1	5	0	8		62.5
Water body	0	0	0	0	3	3		100
Total producer	5	8	8	5	4	30
Producer accuracy (%)	80	75	75	100	75
Accuracy Assessment for 2020
Bare land	3	0	0	0	1		4	75
Farm land	0	7	0	0	0		7	100
Forest	0	1	6	0	0		7	85.7
Settlement	0	2	0	6	0		8	75
Water body	1	0	0	0	3		4	75
Total producer	10	10	6	6	4		30
Producer accuracy (%)	75	70	100	100	75
Overall accuracy	For 1990 = 83.3		For 2000 = 86.6		For 2010 = 80			For 2020 = 83.3
Overall kappa statistics	0.795 (79.5%)		0.826 (82.6%)		0.746 (74.6%)			0.788 (78.8%)

Sources: own summary for result of landsat images Analysis via arc GIS 10.3 (2023)

The kappa coefficients of the classified LULC classes were 0.795 (79.5%) for 1990, 0.826 (82.6%) for 2000, 0.746 (74.6%) for 2010, and 0.788 (78.8%) for 2020 (Table 3).

Figure 5 showed that the framework and methodology flow chart used to creating the LULC and socio-economic drivers for LULC change (Fig. 5).

3.1. LULC changes of Wayu-Tuka since 1990–2020

Based on the landsat images analysis five LULC categories i.e, bare land, farmland, forest, settlement and water body were classified for the years of 1990–2020 (Table 2).

The year 1990 was the decade when the farm land of the study area constitute about one-third of 36.6% (147.8851 Km²) of the total area of the district whereas bare land, settlement, forest, and water body with the respective proportion of 28.1% (113.678 Km²), 12.1% (49.006 Km²), 22.3% (90.0099 Km²) and 0.6% (2.46 Km²).

Table 3

LULC classes of Wayu-Tuka District in 1990–2020
No	LULC Classes	1990		2000		2010		2020
No	LULC Classes	Area (Km²)	%	Area (Km²)	%	Area (Km²)	%	Area (Km²)	%
1	Bare land	113.678	28.1	43.206	10.7	39.264	9.7	3.727	1
2	Farm land	147.8851	36.6	216.0076	53.4	209.0008	51.7	227.0031	56.2
3	Forest	49.006	12.1	33.009	8.2	16.006	4	10.549	2.6
4	Settlement	90.0099	22.3	104.0088	25.7	138.7958	34.3	148.00	36.6
5	Water body	2.46	0.6	8.054	2	1.219	0.3	15.007	3.7
6	Total	404.209	100	404.209	100	404.209	100	404.209	100
Source: Analysis via Arc GIS 10.3,2023

In 2000, the farmland accounted the largest proportion of the total area (about 53.4%) whereas the other land use types showed a slight decrease from the initial in 1990. In fact, the year 2000 was also a period of bare land and forest cover with the respective proportion of 10.7% and 8.2% respectively indicated that significant decrease from the area 1990 (Table 2).

In 2010, except settlement, all the rest of LULC types decreased from the previous period (2000). In this year, bare land 9.7%, farmland 51.7%, forest 4%and water body 0.3% accounted in this study area.

Again in 2020, farmland with accounted the largest area share in percentage of the study site and followed settlement that covered 36.6% (Table 2).

Figure 6 below displays the five LULC types between1990-2020 from left to right. The first map showed that LULC classification of TM 1n 1990 image. In this result, the majority of the District was under farm land 36.6%, bare-land 28.1%, settlement 22.3%, forest coverage 12.1% and water body 0.6% respectively. For the image TM 2000, the result of LULC classification shows that the utmost part of LULC from all classes goes to farm land and settlement areas, which cover 53.4% and 25.7% total area of the district respectively. Bare land, forest coverage and water body 10.7%, 8.2% and 2% respectively. The least area was covered by water body 2% from altogether classes in the study area. Regarding the result of LULC classification for ETM 2010 image, presented that the greatest part of LULC from all classes drives to farm land and settlement areas, which covers 51.7% and 34.3% .Bare land and forest land covered 9.7% and 4% separately. The least area was covered by water body 0.3% from altogether in the district. Finally, the result of LULC for the OLI of 2020 image similarly, indicated that the greatest part of LULC from all classes drives goes to farm land and settlement areas, which covers 56.2% and 36.6% of the total area of the study area respectively. Water body and forest land covered 3.7% and 2.6% individually. In this period, the least area was covered by bare land which covers 1% from the total area of the classes in the District (Fig. 6).

3.2. Gain and loss analysis of LULC change in 1990–2020

In 1990–2000, area change of LULC for bare land and forest coverage result showed positive while the result of area changes for farm-land, settlement and water body indicated negative change. But, in 2000–2010, among the types of LULC classes of area change settlement showed negative, indicating an increase area change (Fig. 7)

Results of area change (2010–2020) also showed that for bare land, farm land, forest land, settlement, and water body 35.537 km²,-17.9951,5.457 km²,-9.2042 km² and − 13.788 km² respectively. Finally, the area change for the period 1990–2020 indicated that farm land showed that the first largest area change increased (-79.118 km²) and settlement was the second largest area change increased (-57.9901 km²) in the same period. For bare land, forest cover and water body of area change were 109.951 km², 38.457 km² and − 12.547 km² respectively.

3.4. Driving forces of LULC changes

The relationship between dependent variable (driving forces of LULC) and socio-economic variables (age, gender, educational status, and land holding size) were analyzed using correlation analysis (Table 4).

Table 4

correlation coefficient values between SEV and driving forces of LULC
		Ir/nsv mdf	Age	Genders	Edust	Lndsz
Is TRSV & LULC?	Pearson Correlation	1	.008	.020	− .016	-0.14
Age	Pearson Correlation	.008	1	− .039	− .030	.771^**
Genders	Pearson Correlation	.020	− .039	1	− .136^*	− .053
Educational status Land holding size	Pearson Correlation Person Correlation	− .016 -0.14	− .030 .771^**	− .136^* − .053	1 − .079	− .079 1
. Correlation is significant at the 0.05 level (2-tailed). *. Correlation is significant at the 0.01 level (2-tailed)Is TRSV = Is there r/n ship between socio-economic variables and LULC, SEV = Socio-Economic Variables and Edust = Educational status

Pearson’s correlation analysis can be used to explain the correlations between dependent and socio economic independent variables before performing modeling analysis. Similar to this, the Pearson correlation coefficient indicated both positive and negative associations. Positive correlation demonstrated that the dependent variable (in this case, the major socioeconomic variables for driving forces of LULC dynamics) increases with an increase in the value of socioeconomic independent variables, whereas negative correlation demonstrated a decrease in the dependent variable with an increase in the value of socioeconomic independent variables for the major driving forces of LULC dynamics. But in the case of negative correlation for independent variable of educational status and Land holding size showed that when the number of an educated population increased the driving forces especially anthropogenic driving forces of LULC dynamics decreased and when the number of Land holding size population increased the driving forces especially anthropogenic driving forces of LULC dynamics decreased and when the number of Land holding size population decreased the driving forces especially anthropogenic driving forces of LULC dynamics increased. The figurative number one (1) correlation showed that a perfect correlation that value of one variable can be determined exactly by knowing the value on the other variable (Table 4).

After the Pearson’s Chi-square was used to identify the main socio-economic determinants correlation with driving forces of LULC dynamics were important. According to (Song & Yong, 2022) binary logistic regression model examined the relationship between the (dependent) and the various socioeconomic (independent) variables which estimated the driving forces of LULC of the independent (explanatory) variables on the dependent (response) variable.

3.5. Categorical Codings and baseline used in the Model

The independent categorical Variables in the model coded for educational status of HHs categorized into an illiterate coded 0, high school coded 1, some colleges coded 0 and respondents who completed Undergraduate recorded as 1 whereas for Genders of respondents were coded for male 0 and for female coded 1, and finally for Land holding size of respondents who have no land were coded as 1 and respondents who owned land were coded as 0 (Table 5).

Table 5

categorical independent Variables Codings
Independent variables		Frequency	Parameter coding
Independent variables		Frequency	(1)	(2)	(3)
Educational status	Illiterate	93	.000	.000	.000
	High school	79	1.000	.000	.000
	Some colleges	28	.000	1.000	.000
	Undergraduate	21	.000	.000	1.000
Gender	Male	189	.000
Gender	Female	32	1.000
Land holding size	No land	127	1.000
	Own land	94	.000
Sources: Analysis through SPSS version 20 (2023)

The below section of the output, headed baseline model for driving forces for LULC dynamics was the result of the analyzed without any of this model’s independent variables (i.e socio-economic variables Age, Gender, Educational status, and Land holding size ) were not used in the Block 0 (i.e baseline model). The expected output of major driving forces of LULC for socio-economic variables those would be selected for natural forces are encoded as 0 whereas for those who selected anthropogenic forces were the driving forces for LULC dynamics classified as 1. Baseline model would use as a bench mark for comparing the model with the result of predicted variables included and the next classification Table^a,b showed that the overall percentage correct is 59.3% (Table 6).

Table 6

dependent variable of driving forces for LULC dynamics Classification Table^a,b
Observed			Predicted
			Driving forces for LULC dynamics		Percentage Correct
			Natural forces	Anthropogenic forces
Step 0	Driving forces for LULC dynamics	Natural forces	0	90	.0
		Anthropogenic forces	0	131	100.0
	Overall Percentage				59.3
a. Constant is included in the model. b. The cut value is .500

3.5.1. Goodness fit Statistics of the Model

Goodness-of-fit statistics showed that the study to determine whether the model adequately describes the data (Archer & Lemeshow, 2006).Omnibus Tests of Model Coefficients was used to test the model fit (Data, 2022). The model is significant and the likelihood ratio chi-square test indicated that the full model is a significant improvement in fit over a null model, x²(23.971), P < 0.001. If the model is significant, this shows that there is a significant improvement in fit as compared to the null model, hence the model is showing a good fit (Dash & Paul, 2021). Therefore, the full model has a significant prediction showed x² = 23.971; df = 5, and P < 0.001 (Table 7).

Table 7

omnibus Tests of Model Coefficients
		Chi-square	df
Step 1	Step	23.971	5
	Block	23.971	5
	Model	23.971	5

Sources: Model Analysis via SPSS version 20 (2023)

The Hosmer and Lemeshow test statistics indicates a poor fit if the significance value is less than 0.05 ( Nattino et al., 2021;Boateng & Abaye, 2019; Fagerland & Hosmer, 2017).In this test; Hosmer and Lemeshow model is well fitted because the chi-square value is 4. 450, P = 0.814 which is greater than 0.05.As a result of this, Hosmer and Lemeshow Test could not reject the null hypothesis of model appropriateness. This showed that socio-economic variable were the driving forces for LULC dynamics (Table 8).

Table 8

Hosmer and lemeshow test
Step	Chi-square	df	Sig.
1	4. 450	8	0.814
Sources: Model Analysis via SPSS version 20

According to Table 9 of Contingency Table for Hosmer and Lemeshow Test indicated that the model adequately fits the data. As anyone can see, there is no difference between the observed and predicted model (Fagerland & Hosmer, 2017). As a result of this, both values are almost the same.

Table 9

contingency for Hosmer and Lemeshow Test
		Driving forces for LULC dynamics = Natural forces		Driving forces for LULC dynamics = Anthropogenic forces		Total
		Observed	Expected	Observed	Expected	Total
Step 1	1	15	15.311	9	8.689	24
	2	12	13.985	11	9.015	23
	3	16	13. 147	8	10.853	24
	4	9	9.912	13	12. 088	22
	5	8	8.365	13	13.635	21
	6	10	8.989	14	15.011	24
	7	8	8.398	17	16.602	25
	8	6	6.052	17	16.948	23
	9	6	4.587	18	19.143	24
	10	0	1.254	11	9.746	11

Sources: Collected data analyzed through SPSS version 20 (2023)

For more clarification as there is no much difference between observed and expected driving forces of LULC dynamics for both Natural forces and anthropogenic forces of the Contingency for Hosmer and Lemeshow Test improved that the model adequately fits the data clearly displayed with line graph (Fig. 8).

The model summary result shows that the pseudo R-square can be used as approximately difference in the criterion variable usually, used is Nagelkerke R² and an adjusted version of the cox & Snell R Square that adjust the scale of the statistics to cover the full range from 0 to 1(Burke1 & Collins4, 2018; Walker & Smith, 2016) which is fitted for model summary below. Then, the result indicated that 13.9% change in the dependent variable can be accounted to the predictor variables (i.e. Table 10).

Table 10

model summary
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square
1	274.749^a	0.103	0.139
a. Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001.

3.5.2. Variables of classification table^a in the Equation for step 1

The percentages in first two rows i.e whether socio-economic variables are driving forces for LULC dynamics or not choose Natural forces and Anthropogenic forces provided information regarding specificity and sensitivity of the model in terms of predicting group membership on the dependent variable (Boateng & Abaye, 2019; Nattino et al., 2021). The model correctly classified 66.1% of cases overall (Table 11).

True negative rate refers to the percentage of cases observed to fall into non-target (or reference) category (Hellmich et al., 2011). The respondents who choose natural force for driving forces of LULC dynamics correctly predicted was 47.8% by the model to fall into the group. In other way the classification predicts that the respondents would choose anthropogenic force.

Specifically, it represents the information on the degree observed outcomes are predicted by the model used.

Sensitivity which is also called true positive rate is the percentage of cases observed to fall in the target group i.e Y = 1 in this cases those who selected anthropogenic force who were correctly predicted by the model fall into that group (Predicted to select anthropogenic force) and the sensitivity for the model is 78.6%.

As a general the accuracy rate was very good at 66.1% since the overall percentage correctly classified respondents’ selected socio-economic variables were the driving forces for LULC changes based on the model (Table 11).

Table 11

classification Table^a for step 1
	Observed		Predicted
			Driving forces for LULC dynamics		Percentage Correct
			Natural forces	Anthropogenic forces
	Driving forces for LULC changes.	Natural forces	43	47	47.8
		Anthropogenic forces	28	103	78.6
	Overall Percentage				66.1
a. The cut value is .500

3.5.3. Variables in the Equation of the Model

Table 12 provided the regression coefficient (B), Wald statics (to test the statistical significance) and the all-important Odds ratio (Exp (B) for each variable category and also showed that the relationship between the predictors and the outcome. Considering the first results for educational_.status (1–3), there is a highly significant overall effect (Wald = 16.287, df = 3, p < .000) B (Beta) is the predicted change in Log Odds for one unit change is the ratio of probability.

Then, Exp (b) for the variables Gender is 0.869 and the researcher can say that the odds a respondents choosing anthropogenic forces offering Gender are 0.869 times higher than a person those anthropogenic forces is 86.9% higher than people who choose Natural forces. Exp (b) for the variables Educational status (1) is 2.596. The exp (b) is 2.596 times more likely that people who are not an educated. The chance that the people who are an educated the level of high school choose the anthropogenic forces for LULC dynamics is 259.6% higher than people who are not educated high school. For exp (b) Educational status (2) categorical variable is 2.610. The chance that the people who are an educated the level of some colleges choose the anthropogenic forces for LULC dynamics is 261% higher than people who are not educated some colleges. In the case of exp (b) Educational status (3) is 7.867. The chance that the people who are an educated the level of Undergraduate choose the anthropogenic forces for LULC dynamics is 786.7% higher than people who are not educated the level of undergraduate which do not offer Natural forces with a 95% CI of 2.146 to 28.843.The Exp (b) for the continuous variable Age is 1.032.The findings stated by ( (Millington, 2007) ) showed that the aging of the study area is driving the observed change (from a human-activity perspective), selecting variables solely on the basis to provide adequate predictive statistical models of change and poor predictive performance of the socioeconomic variables highlights the problems of using aggregated socioeconomic variables i.e selective socio-economic variables is more proffered. In this study, the coefficient of the continuous independent variable value of exp (b) is almost equal to 1, which fitted the subjects who present higher values of the variable have the same odds of success as the subjects who present lower values (i.e that independent variable does not have a significant influence on the response variable) (Table 12).

Table 12

variable in the equation
Socio-economic independents		B	S.E.	Wald	df	Sig.	Exp(B)		95% C.I.for EXP(B)
Socio-economic independents		B	S.E.	Wald	df	Sig.	Exp(B)		Lower	Upper
Step 1^a	Age	0.031	0.014	4.945	1	0.026		1.032	1.004	1.060
	Gender(1)	− .141	0.410	0.118	1	0.732		0.869	0.389	1.939
	Educational_.status			16.287	3	0.001
	Educational_.status(1)	0.954	0.322	8.760	1	0.003		2.596	1.380	4.881
	Educational_.status(2)	0.959	0.462	4.314	1	0.038		2.610	1.055	6.452
	Educational_.status(3)Landholding size	2.063 -0.139	0.663 0.261	9.684 -0.036	1 1	0.002 0.000		7.867 0.654	2.146 0.530	28.843 0.377
	Constant	-1.357	0.560	5.869	1	0.015		0.257
a. Variable(s) entered on step 1: Age, Gender, Educational status and land holding size.

The figure 8 indicated that the accurate of the model in classified the cases. This plot showed that the frequency of categorizations for different predicted probabilities and whether they were ‘1’which represented ’Anthropogenic forces or ‘no’ represented ‘Natural forces’ categorizations with symbolized ‘0’. This provided a useful visual guide to how accurate the model was by displaying how many times the model would predict a ‘1’ outcome based on the calculated predicted probability when in fact the outcome for the participant was ‘0. If the model is good at predicting the outcome for the cases should be seen a bunching of the observations towards the left and right ends of the graph. This plot would show that where the event did occur (Anthropogenic forces was achieved, as indicated by a ‘1’ in the graph) the predicted probability was also high, and that where the event did not occur the predicted probability was also low. The below graph shows that quite a lot of cases are actually in the middle area of the plot Figure 8). So while the model identifies that Age, Gender, Educational status (1-3) and landholding size are significantly related with the driving forces for LULC dynamics outcome, and indeed can explain 13.9% of the variance in outcome (quoting the Nagelkerke pseudo-R²).

3.6. Socio-economic of driving forces of LULC changes

For socio-economic data; research questions distributed for HHs, focal group discussion, key informant interviews and field data collection were analyzed. The approach is all-encompassing by means of uniting many different data sources it is endeavored to quantitatively and qualitatively describe the continuing to classify the main drivers of LULC changes.

The following survey question was raised for selected sample HHs of the study area. This question which distributed and collected from selected sample HHs at the study area was: Is socio-economic variables (Age, Gender, educational status, and land holding size) affect for the driving forces LULC dynamics (Natural forces or anthropogenic forces)?

In this study, the researcher analyzed socio-economic variable for driving LULC changes dynamics in the study area. Thus, the socio-economic variables for drivers of LULC change in the study area were age, gender, educational status and landholding size. These independent variables were analyzed one by one below:

This question was raised for selected respondents ‘is socio-economic variables of Age, affect for the driving forces LULC dynamics in your village? For this survey question almost more than sixty percent of the respondents’ i.e. 62% replied that Age is driving forces for LULC dynamics in their village among this: 25.8% fall under 31–40 age group and the rest .3.2%, 9%,10%, ,and finally 13.6% were found 61–70, 51–60, 20–30, 41–50 under the age group respectively (Table 11).

Table 13

Is socio-economic variable factor for LULC dynamics? * Age of respondants Cross tabulation
		Age of respondants
		20–30	%	31–40	%	41–50	%	51–60	%	61–70	%	Total	%
Is socio-economic variable age is driving forces for LULC dynamics?	No	13	5.9	32	14.5	25	11.3	10	4.5	5	2.3	85	38
	Yes	22	10	57	25.8	30	13.6	20	9	7	3.2	136	62
Total		45	15.9	89	40.3	55	24.9	30	13.5	12	5.5	221	100
Source: Survey question analyzed using SPSS, 2023

Regarding the Gender of the respondents; the question was raised for selected respondents ‘Is socio-economic variable Gender is a driving forces for LULC dynamics in your village? For this survey question more than eighty percent of the respondents’ i.e. 80.5% replied that Gender specifically males were the driving forces for LULC dynamics in their villages where as 19.5% were females for driving forces of LULC dynamics in their village, and finally 39.4% respondents were replied Natural forces for driving forces of LULC dynamics not gender and they selected ‘No’ while the majority of the respondents about sixty percent of the respondents were replied that gender was a socio-economic variable which facilitate for driving forces of LULC dynamics (Table 14).

Table 14

Is socio-economic variable Gender is driving forces for LULC dynamics? * Gender of respondants Cross tabulation
		Gender of respondants
		Male		%	Female	%	Total %
Is socio-economic variables are driving forces for LULC dynamics?	No		72	32.6	15	6.8	87 39.4
	Yes		106	48	28	12.7	134 60.6
Total			178	80.5	43	19.5	221 100
Source: Survey question analyzed using SPSS, 2023

Concerning the Educational status of the respondents; the question was asked for selected respondents ‘Is socio-economic variable Educational status is a driving forces for LULC dynamics in your village? For this survey question 19.5% of the respondents were replied Educational status cannot be the driving forces for LULC dynamics.

Lastly 39.4% respondents were replied that Natural forces for driving forces of LULC dynamics not educational level and they selected ‘No’ while the majority of the respondents similar to the gender about sixty percent of the respondents were replied that educational status was a socio-economic variable which facilitate for driving forces of LULC dynamics regarding landholding size as the number of respondants No lands increase driving forces for LULC dynamics also increases (Table 15) and (Table 16) respectively.

Table 15

Is socio-economic Educational status is driving forces for LULC dynamics? * Educational status of respondants Cross tabulation
Variables		Educational status of respondants								Total	%
Variables		Illitrate	%	High School	%	Some colleges	%	Undergraduate	%	Total	%
Is socio-economic variables are driving forces for LULC dynamics?	No	44	19.9	29	13.1	10	4.5	4	1.8	87	39.4
	Yes	49	22.2	50	22.6	18	8.1	17	7.7	134	60.6
Total		93	42	79	35.7	28	12.7	21	9.5	221	100
Source: Survey question analyzed using SPSS, 2023

Table 16

Is socio-economic variables are driving forces for LULC dynamics? * Land holding size of respondants Cross tabulation
Variables		Land holding size of respondants
		No land	%	Own land	%	Total	%
Is socio-economic variables are driving forces for LULC dynamics?	No	63	28	48	21	111	50
	Yes	64	28	46	20	110	50
Total		127	46	94	41	221	100
Source: survey question analyzed using SPSS, 2023

3.7. Discussion

According to respondents explanations the HHs age group play a great role for the reason behind of driving forces of LULC dynamics especially for developing countries because when their age group reach the above 20 years; they want to own land individually in illegal way which enhanced the alarming rate of LULC. This result almost similar with the work of (B. B. Babiso, 2016) on the title of “Socioeconomic Driving Forces of Land use/Cover Dynamics and its Implications in Wallecha Watershed, Southern Ethiopia” Table 13 also improved the reason for the percentage showed under each age group (Table 13).

Some of an educated HHs answered that 1.8% Undergraduate, 4.5% some colleges, 13.1% also educational status is not a driving forces for LULC dynamics in their villages respectively. Among the respondents 7.7% Undergraduate, 8.1% some colleges, 22.6% High School and 22.2% Illitrate replied that Educational status is a driving forces for LULC dynamics in their village respectively. This aspect agrees with other works from Ethiopia (Gessesse & Bewket, 2014) on title Drivers and Implications of Land Use and Land Cover Change in the Central Highlands of Ethiopia, mentioned that based socioeconomic characteristics of the surveyed households, 23% of the respondents were illiterate. LULC changes are the result of human influences, biophysical drivers and natural processes ( Shiferaw et al., 2019;Mekonnen et al., 2018; Mlotha, 2018; Nelson et al., 2005).

The Focus group discussions were performed among the selected household heads and local peoples, nevertheless of their social situation. According to the ideas raised among the Focus group discussion regarding “Is socio-economic variable Gender is a driving forces for LULC dynamics?”; As household heads and local peoples stated that the main reasons for a great change and urgent decline of LULC dynamics in the study area was the independent variable Gender especially rather than females; males were responsible for land cover conversion from forest to agriculture and settlement. The results of the focus group discussion indicated that LULCCs for the past derived from the priority of the males based on the ideas raised from them.

The last questions were raised for Informal discussion (Interview): “Is socio-economic variable Educational level is a driving force for LULC dynamics in your village?” For those questions in common analyzed, the key informant interviews were directed involving the elder peoples who know and lived in the study area for a long time to develop more reliable information on the changes and drivers of land use change at the study area. The results of the Informal discussion (Interview) which was almost similar to focus discussion indicated that LULCCs for the past and the present situation changes key solution were increasing an educated a number of population especially for developing countries (Haindongo, 2022; Karaya et al., 2021; Kgaphola et al., 2023). According to these selected interviewers the level of education status directly related with driving forces for LULC dynamics which showed that as the education status increased the driving procedures behind forest decline, one specific recent trend, increased agricultural production has crushed the forest through several mechanisms would be decreased. Then, education status was the top socio-economic independent variable among the driving forces of LULC dynamics. A straight line on a graph indicates that dependent variable is directly proportional to independent variables which were represented with green color and brown colors. These graphs showed that among the independent variables educational status is a driving force for LULC dynamics for the HHs in their village selected ‘Yes’ represented with straight line on a graph by green colors and the respondents who were replied that educational level is not driving forces of LULC dynamics selected ‘No’ represented with straight line on a graph by green brown colors (Fig. 9).

This study analyzed the main socio-economic variables for driving forces of LULC dynamics of Wayu-Tuka District. Data were obtained from landsat images classified via Arc GIS to develop LULC Maps of the Wayu-Tuka District. Accuracy assessment and kappa coefficient were also used to approve the accuracy of the classified LULC, and farm land, settlement, bare land, forest land, and water body were the major LULC classes in the District. Forest cover in three decades (1990–2020) decreased from 12.1% in 1990 to 2.6% in 2020 in the study area. For socio-economic data; research questions distributed for HHs, focal group discussion, key informant interviews and field data collection were analyzed. The Pearson’s correlation used to identify the main socio-economic determinants correlation of independent variables with driving forces of LULC dynamics in selected villages were investigated. The result of the study revealed that the independent variables (i.e. Age and Genders) have positive sign Pearson Correlation with driving forces of LULC dynamics; this implied that when these independent variables increase the driving forces of LULC dynamics also increases whereas educational status has a negative correlation which showed that when the number of an educated population increased the driving forces of anthropogenic forces for LULC dynamics decreased and vice versa. Binary logistic regression model examined the relationship between the (dependent) and the main socioeconomic (independent) variables. A logistic regression was performed to ascertain how variables, such as Age, Gender, Educational status and landholding size are the driving forces for LULC change (Natural forces or anthropogenic forces).The logistic regression model was statistically significant (x² = 23.971, df = 5, P < 0.001).The model explained 13.9% (Nagelekerke R²) of the variance in the driving forces for LULC dynamics and correctly classified 66.1% of cases. Age, Gender and Educational status significantly determine driving forces for LULC dynamics. The driving force for LULC dynamics has the greatest chance to choice the anthropogenic forces. Survey question was raised for selected sample HHs of the study area, and for this survey question almost more than sixty percent of the respondents’ i.e. 62% replied that Age is driving forces for LULC dynamics in their village among this: 25.8% fall under 31–40 age group and the rest .3.2%, 9%,10%, ,and finally 13.6% were found 61–70, 51–60, 20–30, 41–50 under the age group respectively. Finally, the result of this study also revealed that 39.4% respondents were replied that Natural forces for driving forces of LULC dynamics not educational level and they selected ‘No’ while the majority of the respondents similar to the gender about sixty percent of the respondents were replied that educational status was a socio-economic variable which facilitate for driving forces of LULC dynamics. Thus, concerned bodies should minimize the driving forces of LULC dynamics especially anthropogenic forces to rehabilitate and restoration of landscape in Wayu-Tuka District, Western Ethiopia.

Competing Interests

The authors declare that they have no conflicts of interest.

Funding Statement

No funding was received for conducting this study.

Data Availability Statements

The data that has been used is confidential.

Author Contribution Statement

Jembere Bekere Kenea, Professor Fayera Senbeta Wakjira, and Dr. Abren Gelaw Mokonen share contribution to this manuscript. Jembere Bekere Kenea collected the data, analyzed, interpreted the data and wrote the manuscript. Professor Fayera Senbeta Wakjira edited, conceived and designed the manuscript. Finally, Dr.Abren Gelaw Mokonen also analyzed and interpreted the data. Both Professor Fayera Senbeta Wakjira and Dr. Abren Gelaw Mokonen supervised the article.

Additional Information

No additional information is available for this Article.

Acknowledgement

The authors wish to thank the reviewers of this article for their critical comments to the improvement of the article and also we thanks Arba Minch University (Ethiopia) acknowledged for its financial support.

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Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia

Status:

Journal Publication

Version 1

Abstract

Figures

1. INTRODUCTION

2. Study Area and Research Methods

2.1. Description of the Study Area

2.2. Materials and Methods

2.2.1. Sources and method of data collection

2.2.2. Household Survey data

2.2.3. LULC change data

2.2.4. Method of data Analysis

2.2.5. Results of Accuracy Assessment

3. Results and discussion

3.1. LULC changes of Wayu-Tuka since 1990–2020

3.2. Gain and loss analysis of LULC change in 1990–2020

3.4. Driving forces of LULC changes

3.5. Categorical Codings and baseline used in the Model

3.5.1. Goodness fit Statistics of the Model

3.5.2. Variables of classification table^a in the Equation for step 1

3.5.3. Variables in the Equation of the Model

3.6. Socio-economic of driving forces of LULC changes

3.7. Discussion

4. Conclusions

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia

Status:

Journal Publication

Version 1

Abstract

Figures

1. INTRODUCTION

2. Study Area and Research Methods

2.1. Description of the Study Area

2.2. Materials and Methods

2.2.1. Sources and method of data collection

2.2.2. Household Survey data

2.2.3. LULC change data

2.2.4. Method of data Analysis

2.2.5. Results of Accuracy Assessment

3. Results and discussion

3.1. LULC changes of Wayu-Tuka since 1990–2020

3.2. Gain and loss analysis of LULC change in 1990–2020

3.4. Driving forces of LULC changes

3.5. Categorical Codings and baseline used in the Model

3.5.1. Goodness fit Statistics of the Model

3.5.2. Variables of classification tablea in the Equation for step 1

3.5.3. Variables in the Equation of the Model

3.6. Socio-economic of driving forces of LULC changes

3.7. Discussion

4. Conclusions

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

3.5.2. Variables of classification table^a in the Equation for step 1