Mound abundance, the livelihood impacts and determinant factors of termites in Meta Robi district, central Ethiopia

Abstract

This study aimed to assess mound abundance, the livelihood impacts of termites, and determinant factors in the Meta Robi District, Ethiopia. A descriptive research design was used for primary data collection from the three selected kebeles. Termite nests were counted from each Goxi (the sub-kebele and the smallest unit in Ethiopia) using a transect line. In addition, 190 household heads were selected randomly from each kebele, and a questionnaire survey was used to collect primary data from the respondents. Termite nests were abundant in Warabo (7.25.71 per 1000 m²), but scarce in Warke Walensu Kebele (2.25.50 per 1000 m²). Teff (49.5%) and wheat (32.6%) were the most severely affected crops by termites. The mean annual hectare of land and quintals of teff and wheat damaged per household was higher in Warabo Kebele (p < 0.05). The annual economic loss from termites in crops was higher in Warabo (4722.23 ± 869.67 ETB and 4396.43 ± 852.65 ETB from teff and wheat per household, respectively). Agroecology, deforestation, overgrazing, and crop types were the factors that determined termite abundance and its impact on farmers' livelihoods. Mound opening and smoking are the management methods currently used, but no government support exists. Therefore, integrated and effective termite pest management is critical for long-term food security in the study area.

1. Introduction

Termites are social insects that belong to the order Isoptera, consisting of about 3000 species with 281 genera and seven families [1]. They are predominantly distributed in tropical and subtropical African countries [2], mainly diverse and abundant in lowland equatorial forests [3]. Termites are the primary consumers of cellulose and lignocelluloses, the most abundant biomolecules on land. Several studies have grouped the feeding behaviours of termites into five main classes. These include grass/litter feeders, which eat grassy litter, grass, and dung; wood feeders, which eat dead/live wood in litter or standing form; soil wood feeders, which eat highly decomposed wood at the soil interface; soil feeders, which eat humified materials within the dirt, and polyphagous species, which consume a variety of organic materials [4,5].

Termites destroy trees and crops, and the impact is strongly related to the geographic region. The impact of termites appears most acute in tropical and subtropical regions, where rainfall is low, and a dry savanna type is prevalent, and this causes severe problems in the growth of nurseries and young tree plantations [6]. Termites damage crops, plants, shrubs, and other trees mainly when they are affected by fire, disease, drought, or other insect pests [7]. It is also a significant pest in dry areas, attacking a variety of grains, cereals, and horticultural crops [2]. However, not all termite species harm crops and forests [8]. The genera of termites considered pests in Africa are Odontotermes and Macrotermes [9]. Harris [9] attributes all losses to members of the subfamily Macrotermitinae, all of which are fungus growers with large nests in mounds or underground.

Termite pest control employs both chemical and traditional methods. Chemical control methods include soil treatment, seedling treatment before transplanting, and baiting techniques, as well as chemicals like chlorpyrifos, imidacloprid, and fipronil [6]. The traditional control methods are mainly related to silvicultural practices or plantation management, and this should be considered before the chemical intervention is attempted because it has a sound basis in the principles of ecology [5,7,10].

Ethiopia is one of the tropical countries where termites cause significant damage to crops and pasture lands, and it is a major pest of the country's growing crops, including living trees [11]. The damage is particularly more severe in rain-fed crops and during dry periods or drought seasons [12]. Maize, wheat, teff, and millet are the crops primarily impacted by termites [13]. This results in decreased crop yield productivity and loss of economic values of crops [14]. It reduces crop yield by cutting young crop stems near the base during the growth stage, feeding on edible crop parts, and covering the outer side of crop leaves with soil sheeting [15].

Few studies have been conducted on termite abundance, distribution, and the associated economic problems in various parts of Ethiopia. For example, a study conducted in Manasibu District, Western Ethiopia [15], Lalokile District, Kellem Wollega Zone [16], Assosa District [17], and the Montane Ecozone of Northwestern Ethiopia [18] revealed that termites are the primary agricultural pest. However, its effect depends on the area's ecology and human activities. The Meta Robi District is one of the areas with high crop production, and termites have a significant impact on crop yield. Although limited studies on termite abundance and livelihood impacts have been conducted by various researchers in various parts of Ethiopia, there is no scientific information on this issue in the Meta Robi District yet. Therefore, the present study aimed to assess the mound abundance, the livelihood impacts of termites, and the main factors determining the population density, with particular emphasis on the major crops produced in the study area.

2. Materials and methods

2.1. Description of the study area

This study was conducted in the Meta Robi District, West Shoa Zone, Oromia National Regional State, Ethiopia. It is located 112 km from the capital, Addis Ababa, toward the west between 9°13′N latitude and 38°22′E longitude at an elevation of 1500–3000 m.a. s. l. The area is characterized by mountainous, plateaus, and sloppy lands. The district has three agroecologies, namely Dega (30%), Weinadega (45%), and Kola (25%). The annual temperature in the study area ranges from 15 to 32 °C. The rainfall also ranged between 950 and 1300 mm, 800–950 mm, and 750–800 mm in Dega, Woinadega, and Kola, respectively. The district experiences heavy rainfall from June to August and medium rainfall from September to November. Subsistence Crop farming and livestock production account for the majority of the economy. The most widely grown crops in the district are millet, sorghum, teff, wheat, barley, bean, pea, and oil seeds (e.g., Niger seed and linseed) (Meta Robi District Agricultural Office, 2015 unpublished).

2.2. Study design and sampling site selection

This study employed a mixed research design, incorporating both quantitative and qualitative data collection methods. The Meta Robi District has 23 rural kebeles (peasant associations). A preliminary survey was conducted in the study area in September 2018 to get important information such as rainfall, temperature, topography, and status of termites in the district, which helped to select representative sampling sites. Based on the information obtained, the representative kebeles from the district were chosen using a cluster sampling method. First, kebeles were classified as highly infested, moderately infested, or rarely infested based on agroecology and termite impact in the area. One kebele was selected at random from each cluster for our study. Based on this category, Warabo, Huko Haro, and Warke Walensu Kebeles were selected from highly, moderately, and less impacted areas, respectively. Agriculture occupied approximately 76%, 77%, and 73% of the land in Warabo, Huko Haro, and Warke Walensu Kebeles, respectively (Meta Robi District Agricultural Office, 2015 unpublished). Warabo is a lowland kebele, Huko Haro is a midland kebele, and Warke Walensu is a highland kebele.

Two local administration units referred to as Goxi (the lowest sub-kebele unit) were randomly selected from each kebeles. The residence area and open field in each Goxi were considered to determine the abundance of termite nests in triplications. Teff (Eragrostis tef) and wheat (Triticum aestivum) were purposely chosen for this study to assess the impact of termites on the crops in the area because they are the most widely cultivated crops and provide a significant source of income for the people who live in the area. Household heads were selected from each kebele using systematic random sampling techniques for a questionnaire survey [19].

2.3. Data sources and sample size determination

In this study, primary data were used. The farmer household heads provided primary data on the impact of termites on the livelihood of the society in the study area using the predesigned semi-structured questionnaires and focus group discussions, however, information on termite abundance was obtained through field surveys using transect line walks [15]. The questionnaires were prepared in English and translated into Afaan Oromo for more communication with the respondents. The researcher used close- and open-ended questions.

The main components of the questionnaires were the respondents' socioeconomic characteristics, the types of crops grown and damaged by termites, the factors for termite abundance, the stage of crop damage by termites, and the management practices of the problem in the study area. The questionnaires were pre-tested on some respondents who were not part of the primary data collection to refine the reliability and validity of the questions using Cornbach's alpha reliability test. The Cronbach's alpha value greater than 0.70 was an acceptable cut-off point of the internal consistency and reliability test [20]. The questionnaires were distributed to the respondents after further refinement was made based on the feedback obtained from the respondents.

The sample size of the population was determined using Yamane's [21] formula, which is described as n = N/[1+N (e)²], where n represents the sample size, 1 represents the probability of the event occurring (constant), N stands for population size (sampling frame), and e is the margin of error 7% (0.07). The total number of households in the three kebele was 2842. Based on this, the total sample size of the respondents was calculated as 2842/1 + 2842(0.07)² = 190.

The number of respondents in each Kebele was calculated by probability proportion as n = nith * n/N, where nith is the sample size taken by Yamane [21] formula, n represents the number of households in each Kebeles and N represents the total number of households in the kebele (Table 1).

Table 1.

Sample distribution of household heads in selected Kebeles.

Study kebele	Total number of household heads in the kebele	The total number of respondents selected
Warabo	977	65
Huko Haro	912	61
Warke Walensu	953	64
Total	2842	190

2.4. Methods of data collection

Primary data were collected from October through January 2020. First, six sampling sites were selected randomly from each Goxi (three from the residence area and three from the open field). One sampling frame with 10000 m² (100mx100 m) was taken from each site in triplicate using a tape meter. Two transect lines were taken from each frame. Walking across the transect lines; the number of termite nests was counted to determine the termite abundance. This process aided in determining the extent of land damaged by termites in hectares.

Furthermore, a systematic random sampling technique was used to select household heads for a questionnaire survey to assess the livelihood impact of termites in the study area [19]. Besides, focus group discussions (with 5–10 members each) were held at each data collection site to triangulate data obtained by the questionnaire survey.

2.5. Extrapolation of the damaged areas, crop losses, and economic impacts

The average size of teff and wheat farmland damaged by termites was determined by measuring the extent of termite-infested land per household with a tape meter. The amount of crop lost was calculated by considering both termite-infested and non-infested farmlands. The average crop loss was calculated by subtracting the teff and wheat yield obtained per household per year from non-infested areas to infested areas [2,12]. Similarly, the mean economic loss was calculated as the number of quintals of teff and wheat lost per person per year due to termites multiplied by the market price of 1 kg of teff and wheat at the time of data collection. During this time, the price of 1 kg of teff and wheat was 19 and 13.5 Ethiopian Birr (ETB), respectively (ECX, 2020 unpublished).

2.6. Statistical analysis

SPSS software version 24.1 was used to analyze the data. Descriptive statistics such as percentages, mean, and standard deviations were used to summarize the descriptive results. A one-way analysis of variance (ANOVA) was used to ascertain the mean and the extent variation in termite abundance, as well as the damage to teff and wheat (measured in hectares, quintals, and monetary loss) among the study kebeles attributed to termite impacts. The findings were presented in the form of tables and figures.

Binary logistic regression was used to determine factors affecting termite abundance and its impacts in the study area. To obtain the logistic model from the logistic function, we write Z as the linear sum of independent variable factors of the form (Eq. (1)):

$$Z={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots \dots ..+{\beta }_i{X}_i$$ (1)

where the X_i are independent variables of interest and (β′s) represent unknown coefficients of independent variables. The specification or reduced form of the empirical model estimated is as follows (Eq. (2)):

$$Y_i={\alpha }_0+{\beta }_i\sum X_i+{\epsilon }_i$$ (2)

Where, $Y_i$ is a dichotomous dependent variable (termite abundance and effects, specified as yes = 1, 0 = no); ${\alpha }_0$ is the Y-intercept; ${\beta }_i$ is the set of coefficients to be estimated; $X_i$ is the set of explanatory variables hypothesized, based on theory and related empirical work, to influence termite abundance and effects; and ${\epsilon }_i$ is an error term (Eq. (1)). Therefore, the model is denoted as:

$$logit\left(Y\right)={\alpha }_0+{\beta }_1{X}_1+{\beta }_1{X}_2,...,\beta nXn+{\mathrm{\epsilon }}_0$$ (3)

where, Y = dependent variable (termite abundance and its effects or not), X₁, X₂, …, Xn =independent variables (Kebele of the HHs, deforestation, overgrazing, crop types, and management practices of termites), and β = coefficient of the independent variable, and ε₀ = error index (Table 2).

Table 2.

Description of variables used in the binary logistic regression model.

Variables	Type	Description	Value	Mean
Dependent variable: Abundance & and termite effect	Dummy	Termite abundance and effect on livelihood	0 = No, 1 = Yes	0.76
Independent variables:
Kebele	Categorical	Kebele of the household heads	1 = Warabo, 2 = Huko Haro, 3 = Warke Walensu	1.97
Deforestation	Categorical	The presence of deforestation in the area	0 = No, 1 = Yes	0.64
Overgrazing	Categorical	The presence of overgrazing in the area	0 = No, 1 = Yes	0.62
Crop types	Categorical	The type of crops grown in the area	0 = No, 1 = Yes	2.24
Management practices	Categorical	Management system being used by farmers	0 = No, 1 = Yes	2.45

In addition, the Wald test is used to test the of significance individual regression coefficients, which is explained by maximum likelihood estimates as indicated in the following.

$$\mathrm{W}=\frac{{\beta }^{\bigwedge }‐\mathrm{\beta }0}{\text{SE}\left({\beta }^{\bigwedge }\right)}᷈\sim \mathrm{N}\left(1,0\right)$$

where β^ represents the Maximum Likelihood Estimation (MLE) of the coefficient, β0 represents the parameter of interest, usually 0, as we want to test whether the coefficient is different than zero or not, and SE represents the Standard Error of MLE.

3. Results

3.1. Socio-demographic characteristics of the respondents

This study included 190 participants (79.5% males and 20.5% females). Of these, 40.5% were found to be between the ages of 21 and 40, followed by 41–50. Most of them (44.7%) received at least adult education, followed by primary education (22.6%). Approximately 89.5% of them were married and had families. All of the respondents were farmers, and their livelihoods were based on crop production and livestock rearing (Table 3).

Table 3.

Socio-demographic characteristics of the respondents.

Items	Alternatives	Number of respondents	Percentage (%)
Kebele	Warabo	65	34.20
	Huko Haro	61	32.10
	Warke Walensu	64	33.70
Sex	Male	151	79.50
	Female	39	20.50
Age	20–30	51	26.80
	31–40	77	40.50
	41–50	51	26.80
	>50	11	5.80
Educational background	Non-educated	31	16.30
	Adult education	85	44.70
	Primary education (1–8)	43	22.60
	Secondary education (9–12)	31	16.30
Marital status	Married	170	89.50
	Widowed	20	10.50
Livelihood background	Crop production and rearing of livestock	190	100.00
Occupation	Farmer	190	100.00

3.2. Types of crops grown in the study area

Teff is the most widely cultivated crop (37.89%) by farmers, followed by wheat (30.5%) and sorghum (11.05%) (Table 4).

Table 4.

The respondent's response regarding the types of crops growing in the study kebeles.

Crop types	Warabo		Huko Haro		Warke Walensu		Total
	№	%	№	%	№	%	№	%
Teff	24	36.92	21	34.43	27	42.19	72	37.89
Wheat	22	33.85	19	31.15	17	26.56	58	30.53
Maize	7	10.77	7	11.48	6	9.38	20	10.53
Sorghum	6	9.23	7	11.48	8	12.50	21	11.05
All	6	9.23	7	11.48	6	9.38	19	10.00
Total	65	100.00	61	100.00	64	100.00	190	100.00

№ represents the number of respondents and % represents the percentage.

3.3. Main reasons for annual crop yield reduction in the area

The majority of respondents (59%) stated that termites are the leading cause of yield loss in annual crops (primarily teff and wheat), followed by soil infertility (20.5%) (Fig. 1). Similarly, focus group discussions confirmed that termites are the most common crop pest in the area.

Fig. 1.

Respondent's response regarding reasons for annual crop loss in the study area.

3.4. Termite abundance in the study area

In total, 142 termite nests were counted across all of the study sites (Fig. 2). Of these, 89 nests were found in open fields and 53 in residential areas. The number of termite nests was highest in Warabo Kebele (77 nests), followed by Huko Haro (43 nests), and lowest in Warke Walensu Kebeles (22 nests). The average number of termite nests was 7.25 ± 1.71, 3.75 ± 0.96, and 2.25 ± 0.50 per 1000 m² in residence areas of Warabo, Huko Haro, and Warke Walensu kebeles, respectively. However, it was 12 ± 1.41, 7 ± 1.41, and 3.2 ± 50.50 per 1000 m² in the open fields of Warabo, Huko Haro, and Warke Walensu kebeles. The number of termite nests varied significantly across sites and study kebeles (p < 0.05) (Table 5 and Fig. 2a–c).

Fig. 2.

The photo of termite mound taken at the three study sites (a. Image taken from Warabo Kebele b. Image taken from Huko Haro Kebele c. Image taken from Warke Walensu Kebele.

Table 5.

Mean ± SD number of termite nests around home and field sites in the study kebeles.

Sites	Termite nest count in 10000m2						p-value
	Warabo		Huko Haro		Warke Walensu
	Range	Mean ± SD	Range	Mean ± SD	Range	Mean ± SD
Home	5–9	7.25 ± 1.71	3–5	3.75 ± 0.96	2–3	2.25 ± 0.50	0.001
Field	11–14	12 ± 1.41	6–9	7 ± 1.41	3–4	3.25 ± 0.50	0.000
p-value	0.002	0.002		0.003		0.008

3.5. Means of the termite's impact in the study area

Farmers explained that termites have been wreaking havoc on the crops (primarily teff and wheat) by covering the outer surface of their crops with soil (27%) and feeding on the edible parts of the crops (22%) (Fig. 3). Most farmers (55%) stated that termites cause significant economic loss by reducing yield quality and quantity (16%).

Fig. 3.

Respondent's response regarding ways termite impacts crops.

3.6. Types and stages of crops damaged by termite

According to most respondents (49.5%), teff is the most regularly damaged crop due to termites at all growth stages, followed by wheat (32.6%). The grains are primarily damaged during the harvesting stage (63.68%) and the maturity stage (24.74%) (Table 6).

Table 6.

The type of crops and stage at which crops are damaged by termites.

Crops damaged	At flowering stage		At maturity stage		At harvesting stage		Total	%
	№	%	№	%	№	%
Teff	13	6.84	25	13.16	56	29.47	94	49.47
Wheat	6	3.16	15	7.89	41	21.58	62	32.63
Maize	2	1.05	3	1.58	11	5.79	16	8.42
Sorghum	1	0.53	3	1.58	10	5.26	14	7.37
All crops	0	0.00	1	0.53	3	1.58	4	2.11
Total	22	11.58	47	24.74	121	63.68	190	100.00

3.7. Extrapolation of termite impact on teff and wheat

The mean amount of teff farmland damaged by termites was highest in Warabo Kebele (0.28 ± 0.05 ha per household) and the least in Warke Walensu Kebele (0.01 ± 0.04 ha per household). Similarly, Warabo Kebele had the highest number of hectares of wheat farmland damaged by termites (0.23 ± 05 ha. per household), followed by Huko Haro Kebele (0.13 ± 0.06 ha per household). The results show a significant difference in farmland damage across the study kebeles for teff (F_20.24; p = 0.002) and wheat (F_67.53; p = 0.001) (Table 7).

Table 7.

The mean annual hectares of teff and wheat lands damaged by termites per household in the study area.

Kebele	Teff		Wheat
	Range	Mean ± SD	Range	Mean ± SD
Warabo	0.15–0.35	0.28 ± 0.05	0.15–0.32	0.23 ± 0.05
Huko Haro	0.05–0.25	0.17 ± 0.05	0.04–0.25	0.13 ± 0.06
Warke Walensu	0.05–0.20	0.099 ± 0.04	0.04–0.15	0.067 ± 0.03
p-value		0.002		0.001

Households in the Warabo Kebele experienced the highest mean annual per capita teff loss due to termite impact (2.49 ± 0.46 quintal), while those in the Warke Walensu Kebele encountered the least (0.89 ± 0.32 quintal). Similarly, the annual per capita loss of wheat due to termite impact was highest in Warabo Kebele (3.23 ± 0.63), followed by Huko Haro Kebele (1.78 ± 0.78). The results indicated a significant variation in per capita teff and wheat biomass loss due to termites among households in each of the three study kebeles (F_23.21; p = 0.05 and F_17.25; p = 0.008, respectively) (Table 8).

Table 8.

The estimated mean annual loss of teff and wheat per household (in quintal) due to termites in the study area.

Kebele	Teff		Wheat
	Range	Mean ± SD	Range	Mean ± SD
Warabo	1.35–3.15	2.49 ± 0.46	2.10–4.48	3.23 ± 0.63
Huko Haro	0.45–2.25	1.50 ± 0.49	0.56–3.50	1.78 ± 0.78
Warke Walensu	0.45–1.80	0.89 ± 0.32	0.56–0.70	1.03 ± 0.87
p-value		0.004		0.008

At the time of data collection, the price of a quintal of teff and wheat was 1900 (69.1 USD) (1 USD = 27.5 ETB) and 1350 ETB (49.1 USD), respectively. The mean annual percapita economic loss from termite impact on teff was highest in Warabo Kebele (4722.23 ± 869.67 ETB (171.72 USD) per household), and lowest in Warke Walensu Kebele (1696.64 ± 599.61 ETB (61.69 USD) per household). Similarly, the mean annual percapita economic loss from termite damage to wheat was highest in Warabo Kebele (4396.43 ± 852.65 ETB (159.76 USD) per household), followed by Huko Haro Kebele (2407.43 ± 1056.77 ETB (87.54USD) per household). The results showed a significant difference in economic loss from the two crops due to termite impact among the households in the three study kebeles (F_72.34; p = 0.001 and F_86.14; p = 0.001, respectively) (Table 9).

Table 9.

The estimated mean annual economic loss (in ETB) per household due to termite impacts in study kebeles.

Kebele	Teff		Wheat
	Range	Mean ± SD	Range	Mean ± SD
Warabo	2565–5985	4731.23 ± 869.67	2835–6048	4360.43 ± 852.65
Huko Haro	855–4275	2850.34 ± 921.99	756–4725	2403.43 ± 1056.77
Warqe Walensu	855–3420	1691.64 ± 599.61	756–945	1390.50 ± 1174.46
p-value		0.001		0.001

3.8. Management practices of termite impacts in the area

All farmers agreed that the government is not taking any control measures to address the termite problem in the study area. However, 40.5% of them stated that mound opening and smoking are the most widely used termite control techniques by farmers. To control the impact of termites, 35.8% of farmers use queen removal and flooding methods (Fig. 4). According to the focus group discussion, removing queen termites may be the best way to control termite impact in the future, followed by flooding, chemical treatment (heptachlor, chlordane, and aldrin), and planting vetiver grass and other termite-repellant plants around the crop.

Fig. 4.

Respondent's response regarding the controlling measures used in the study area.

3.9. Determinant factors for termite infestations and impacts

According to most respondents (44%), cutting trees and clearing land for agriculture were the most significant factors contributing to termite abundance in the study areas, followed by overgrazing and grass depletion (33%) (Table 10).

Table 10.

Factors that aggravating the termite problem in the study area.

Factors	Warabo		Huko Haro		Warke Walensu		Total
	№	%	№	%	№	%	№	%
Deforestation	29	44.62	29	47.54	26	40.63	84	44.21
Overgrazing	20	30.77	22	36.07	21	32.81	63	33.16
The natural increase in termite population	14	21.54	8	13.11	13	20.31	35	18.42
All	2	3.08	2	3.28	4	6.25	8	4.21
Total	65	100.00	61	100.00	64	100.00	190	100.00

The factors determining the termite infestation and its impacts in the study area are presented in Table 11. Kebele, deforestation, overgrazing, and crop types are the factors that positively and significantly influence termite abundance and impacts in the study area. Farmers in the Warabo kebele are 8.343 times more likely to face termite infestation and its consequences [Odd Ratio: 8.343, Confidence Limit: 3.134–22.214] than those in the Warqe Walensu kebele. Similarly, farmers who live in deforested areas are 30.7% [OR: 0.307, CL: 0.154–0.614] less likely to experience termite infestation and its consequences than farmers who live in non-deforested areas. Farmers in overgrazed areas were also 25.6% [OR: 0.256, CL: 0.256–0.994] less likely to face termite infestation and its consequences than those in non-overgrazed areas. Farmers growing teff and wheat are 4.444 and 3.200 times more likely to be affected by termites (OR: 4.444, CI: 1.427–13.845 and OR: 3.200, CL: 1.039–9.852, respectively) than the farmers growing other crops.

Table 11.

Determinant factors for termite infestation and its impacts on the farmers.

Variables	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.
							Lower	Upper
Kebele			21.931	2	0.000
Warabo	2.121	0.500	18.029	1	0.000	8.343	3.134	22.214
Huko Haro	1.350	0.421	10.298	1	0.001	3.857	1.691	8.795
Deforestation	−1.181	0.353	11.170	1	0.001	0.307	0.154	0.614
Overgrazing	−0.685	0.347	3.910	1	0.048	0.504	0.256	0.994
Crop types			17.003	4	0.002
Teff	1.492	0.580	6.620	1	0.010	4.444	1.427	13.845
Wheat	1.163	0.574	4.110	1	0.043	3.200	1.039	9.852
Maize	−0.251	0.603	0.174	1	0.677	0.778	0.239	2.534
Sorghum	−0.118	0.707	0.028	1	0.868	0.889	0.222	3.554
Management			3.679	3	0.298
Chemical	0.229	0.588	0.151	1	0.697	0.795	0.251	2.521
Queen removal	0.520	0.550	0.896	1	0.344	1.683	0.573	4.943
Mound opening	0.560	0.543	1.063	1	0.303	1.750	0.604	5.071
Constant	0.809	1.049	0.594	1	0.441	2.245

a. Variable (s) entered 1: Kebele, Deforestation, Overgrazing, Natural, Crop types, Management.

4. Discussion

Certain termite species are critical pests of growing crops [22], but some termites are rarely primary pests, only damaging plants, shrubs, or trees after they have been damaged by fire, disease, drought, mechanical injury, poor planting practices, or other insect pests [4,9]. In the current study, the majority of respondents (59%) stated that termites are the most dangerous pests of their crops and that human activity has severely harmed the environment. According to Alexandre et al. [23], environmental changes, including human activities, have a significant impact on termite populations, and the diversity and abundance of termites increase with an increase in disturbances related to the reduction of tree density, soil cover, and intensity of trampling by livestock in the area. The abundance of termites and their impact was more severe in open fields than in residence areas, which could be associated with the high fertility of the soil around the residence and the absence of enough nutrients, materials, and other food sources for termites in open fields. It could also be related to better management practices in the residence area. According to Banjo et al. [24], the presence of dung, adequate nutrients, and leftover materials near residences may mitigate termite impact by reducing crop pressure. Similarly, Harris [25] stated that termites consume a wide range of freshly dead or decaying plant materials such as dry grass, leaf litter, decaying wood, dung, and humus, which may reduce termite impact on grain crops. It also agrees with Getahun and Getu [26] and Djirata [15], who identified more termites and their impacts in open fields than in residence areas in the central Ethiopian Rift Valley and Manasibu District in western Ethiopia, respectively.

Termites had a significant effect on all crops in the study areas, though the extent of the effect varied greatly between sites and crops. The high abundance and its impact in Warabo Kebele may be attributed to extensive land clearing for agricultural expansion and overgrazing in the area. According to Wood [12], termite abundance increases with increased grazing pressure and grass depletion. Logan et al. [6] also stated that the geographic region and human activities in the area have a strong influence on the extent of the termite problem and the nature of the loss caused by termites. Termite attacks, for example, are most severe in areas with low rainfall and dry savannah-like environments [27].

The results of the present study also revealed that teff and wheat are the most widely grown and highly impacted crop types by termites in the area, where teff takes the largest share of the damage. This is because termites can easily cover the entire surface of the teff with soil particles. Studies also reported that teff, wheat, and maize are the most widely damaged crops by termites in different areas [13,28]. The study conducted in the central Ethiopian Rift Valley also indicated that maize and haricot beans are the major crops grown in their area with a high susceptibility to termites [26]. It also agrees with the findings of Bulto and Hirpa [17] from Ethiopia's Assosa District. Similarly, maize is the most commonly damaged crop by termites in other African countries. For example, 97% of farmers in western Kenya [7] and eastern Zambia [29] reported that maize is the most susceptible crop to termites. In contrast, Gute [16] reported that millet and sorghum are the most severely affected crops by termites in Kellem Wollega. The difference could be attributed to the agroecological conditions of the study areas, which influence the types of crops grown in the area and the feeding behavior of termites [30].

According to Nyeko and Olubayo [22], several crops are vulnerable to termite attack from seedling to harvest time. Our findings also revealed that termites have a severe impact on crops at all stages of growth, but the problem is especially severe during harvesting. Similarly, Getahun and Getu [26] reported that termites' impact on crops begins at maturity and continues until harvesting, but the most significant damage occurs during harvesting in the central Rift Valley of Ethiopia. The studies also confirmed that almost all of the major crops grown in Ethiopia are attacked by one or more species of termites at some point during their growth (from seedling to harvesting) [14,31]. The research conducted in the central Ethiopian Rift Valley also revealed that maize is heavenly attacked by termites both during the growing and lodging stage [26]. It also corresponds to the findings of Gudeta et al. [32], Akutse et al. [33], and Abdulli [34] in various parts of the country.

The findings also revealed that termite infestation is causing significant crop yield reductions and affecting societal livelihood by reducing crop quantity and quality. Abdulli [14] reported that most termite species attack various crops by reducing the weight of the yields. Shiday et al. [34] also stated that termites cause significant economic loss and food insecurity by reducing crop yield and quality. Getahun and Getu [26] also reported an estimated loss of 18.02 and 10.58 kg per hectare from haricot bean and maize yields before harvest due to termite impact in the central Ethiopian rift valley, which is similar to our finding.

Most farmers responded that they are using indigenous knowledge, such as mound opening and smoking, to control the effects of termites in this study area. This result is consistent with the findings of Djirata [[15], [35]] in western Ethiopia. According to Sileshi et al. [36], sustainable termite management that does not result in ecological damage or loss of ecosystem services ensures long-term risk management. Many studies have confirmed a wide range of termite control in crops and forestry using non-chemical management methods, such as cultural and biological methods in various areas [6,7,37]. Although the control methods they use are preferable, farmers are ineffective in managing termite impacts due to a lack of sufficient skill and government support. According to Yudelman et al. [38], the government's role in controlling the impact of termites on crops and systematically evaluating losses in agricultural activities caused by pests such as termites is critical. Therefore, effective management is critical for long-term control of termite impacts in the area.

Our findings revealed that differences in anthropogenic activities in the study kebeles caused variation in termite abundance and its effects. The termite abundance, total hectares of teff and wheat farmlands, and annual teff and wheat losses per household were all extremely high in Warabo Kebele, where farmers face termite infestation and its consequences 8.343 times more likely than in the other Kebeles. This impact could be due to the lower rate of deforestation, overgrazing, and agricultural activities in the area. Conservation agriculture benefits soil macrofauna by causing less soil disturbance and possibly accumulating more organic matter than traditionally cultivated fields [39,40]. According to Djirata [15], Kouakou et al. [41] and Olugbemi [42], soil-feeding termites, in general, are very susceptible to habitat disturbances and are the most affected by land degradation, making them excellent biological indicators.

Our findings also revealed that deforestation and overgrazing were negatively associated with termite abundance and its consequences in the study area, with farmers living in the deforested and overgrazed areas experiencing 30.7% and 25.6% less likely termite infestation and its consequences than farmers living in other areas. This result could be because intensive land use destroys termite microhabitats, nesting, and feeding sites, and thus reduces termite diversity and density. The studies also indicated that habitat disturbance eliminates the most vulnerable termite species and results in low biodiversity, with only a few dominant species remaining [29,43]. Our findings are consistent with those of Wale and Nega [18], de Paula et al. [44] and Desalegn [45].

The types of crops grown also influence termite abundance and its impact on farmers [46]. Farmers growing teff and wheat face 4.444 and 3.200 times the risk of termite infestations, respectively than farmers growing other crops. This susceptibility could be because the study area uses a highly organic farming system for maize and wheat production. The study also found that organic-based inputs used in organic farming were one of the main reasons these plots attracted far more termites [47]. Termites prefer cellulose materials, which are found in organic materials [48,49]. Similar results have been reported by Ayuke et al. [50].

5. Conclusion

The current study concluded that termites are the most common pest, wreaking havoc on crops and threatening the livelihood of the community in the study area. The abundance of termites and their effects on crops, on the other hand, is not uniform. The problem is particularly severe in open fields, as linked to better management approaches in the residential area. Termite crop damage is causing significant economic loss by reducing annual productivity and crop yield quality. Furthermore, commercially essential crops such as teff and wheat are the most heavily impacted crop types in the study area. Human activities in the study kebeles, deforestation and overgrazing, and the types of crops cultivated by farmers are all factors that contribute to the severity of the termite infestation and its impact on livelihood in the study area. In essence, people resort to traditional management methods independently, without assistance from government or organizational sources. This highlights the need for a comprehensive and integrated approach to ensure long-term sustainable management of termite impacts in the study area.

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The paper contains all of the data used in this study.

CRediT authorship contribution statement

Senessa Daba: Urga, Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mathewos Temesgen: Kebede, Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.