Elephant crop raiding in northern Tanzania: Spatio-temporal trends and damage assessment in villages adjacent to Mkomazi National Park

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Shoo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7093604/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human-elephant conflict (HEC) poses a major threat to biodiversity conservation. This study examined the spatio-temporal patterns and socio-economic impacts of elephant-induced crop damage in Mkonga Ijinyu and Kavambughu villages adjacent to MKONAPA, Tanzania. Data were collected through household surveys (n = 162), key informant interviews, and secondary data records from 2019 to 2023. Results revealed that crop damage was spatially concentrated within 1 km of park boundaries and temporally peaked during harvest seasons from April to July and in October. Maize (88.3%), beans (76.5%), and sunflower (64.2%) were the most frequently damaged crops. Crop raids occurred predominantly at night (98.8%), with elephants destroying up to one acre in a single event. Kavambughu experienced the highest annual loss (527 acres in 2023), while Mkonga Ijinyu reported higher average damage per incident. This finding indicates that crop raiding follows predictable patterns tied to crop maturation, proximity to wildlife habitat, and seasonal forage scarcity inside the park. These behavioral adaptations by elephants amplify conflict intensity and highlight the urgency of targeted interventions. Socio-economically, over half of affected households reported food insecurity and income loss, while all respondents noted reduced school attendance among children demonstrating the broader developmental consequences of HEC. The escalation of elephant-induced crop damage jeopardizes both conservation outcomes and local well-being. This study recommends integrated mitigation strategies including buffer zone reinforcement, adoption of innovative deterrents (e.g., beehive fences and early warning systems), and stronger community engagement. Policy frameworks should prioritize compensation and ecosystem-based planning to foster long-term human-elephant coexistence. Human-elephant conflict Crop raiding Spatio-temporal analysis Mkomazi National Park Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Human-wildlife conflict (HWC) represents one of the most pervasive challenges in contemporary conservation, undermining both biodiversity protection and human well-being, particularly in rural, biodiversity-rich areas where livelihoods depend heavily on natural resources [ 1 , 2 ]. The issue is especially pronounced in tropical and subtropical landscapes, where rapid human population growth, agricultural expansion, and climate-induced changes intensify competition for land and water between humans and wildlife [ 3 , 4 ]. Elephants, due to their ecological needs and behavioral flexibility, are at the forefront of this conflict, generating disproportionate damage in comparison to other wildlife species [ 5 , 6 ]. Elephants are long-lived, wide-ranging megaherbivores with high cognitive capacity and social complexity. As ecosystem engineers, they contribute significantly to landscape structuring and seed dispersal. However, these same traits combined with increasing habitat fragmentation also enable them to exploit anthropogenic landscapes, particularly during periods of resource scarcity [ 7 – 10 ]. Their foraging behavior often brings them into direct conflict with smallholder farmers, leading to extensive crop damage, infrastructure destruction, human injury or death, and in some cases retaliatory killings [ 11 , 12 ]. As a result, human-elephant conflict (HEC) is now one of the most urgent threats to the long-term viability of elephant populations and community-based conservation programs [ 13 ]. Globally, HEC is reported in over 50 countries across Africa and Asia, with at least 37 African countries facing elephant-related damage [ 14 ]. In South Asia, India alone accounts for approximately 60% of the global Asian elephant ( Elephas maximus ) population, and reports over 500 human deaths annually due to elephant encounters, alongside large-scale crop, and property losses [ 15 , 16 ]. In sub-Saharan Africa, the return of elephant populations in several countries, owing to improved anti-poaching measures and international trade bans, has inadvertently led to a resurgence of conflict near protected areas and migratory corridors [ 17 ]. While this recovery is ecologically encouraging, it is socially and politically fraught, particularly where rural communities experience repeated crop failures and inadequate mitigation or compensation [ 2 , 18 ]. In Tanzania home to one of the largest elephant populations in East Africa, the human-elephant interface has grown increasingly volatile in recent decades [ 12 , 19 , 20 ]. From 2009 to 2015, Tanzania experienced a drastic population decline due to poaching, but through strong enforcement and habitat protection efforts, elephant numbers increased from approximately 43,000 in 2014 to around 60,000 by 2021 [ 21 , 22 ]. Unfortunately, this conservation success has not been matched by robust human-elephant coexistence strategies. Many rural communities bordering protected areas such as those adjacent to Serengeti, Ruaha, and Mkomazi National Park (MKONAPA) have reported increasing crop losses, food insecurity, and negative attitudes toward conservation authorities [ 23 – 26 ]. MKONAPA is located in northeastern Tanzania, exemplifies this growing challenge. Its proximity to expanding settlements and farmland in Same District’s Mkonga Ijinyu and Kavambughu villages has created a conflict hotspot. These communities, located within a 1–14 km radius of the park, depend largely on rainfed subsistence agriculture and regularly experience elephant incursions, particularly during the dry season and harvest periods [ 26 , 27 ]. Yet, limited empirical evidence exists on the scale, pattern, and socio-economic impacts of HEC in this specific landscape. Understanding the spatial and temporal dimensions of elephant-induced crop damage is essential for designing effective and context-specific mitigation strategies. Previous studies in other Tanzanian ecosystems have highlighted the significance of factors such as distance from park boundaries, land-use type, seasonality, and community perceptions in influencing both conflict severity and local responses [ 26 , 28 ]. However, such integrated assessments are lacking for Mkomazi, a region that has received relatively little research attention despite growing reports of elephant activity and crop damage. This study aims to fill this critical gap by systematically analyzing the extent, temporal trends, and socio-economic consequences of elephant-induced crop damage in two villages bordering MKONAPA. Specifically, it examines how the proximity to the park, seasonal variations, and household characteristics influence crop damage risk, and how local communities perceive and respond to this challenge. The findings are intended to inform targeted mitigation approaches, support evidence-based conservation planning, and contribute to broader efforts to balance biodiversity conservation with rural development, especially within the framework of the Kunming-Montreal Global Biodiversity Framework and the Sustainable Development Goals (SDGs 11, 13 and 15), which call for inclusive and equitable conservation strategies that recognize human needs and rights [ 29 ]. 2 Methodology 2.1 Study area description Data were collected from two villages Mkonga Ijinyu and Kavambugu located adjacent to the eastern boundary of MKONAPA in Same District, Kilimanjaro Region (Fig. 1 ). MKONAPA (MNP) is located in northeastern Tanzania, lying between latitudes 3°47′ to 4°33′S and longitudes 37°45′ to 38°45′E. The Mkomazi park covers an area of approximately 3,245 km² and is recognized for its diverse ecosystems, including dry savannahs, acacia woodlands, and seasonal river systems. MKONAPA is home to over 400 bird species and more than 90 mammalian species, including populations of African elephants ( Loxodonta africana ), giraffes ( Giraffa camelopardalis ), zebras ( Equus quagga ), and predators such as lions ( Panthera leo ) and leopards ( Panthera pardus ). The park is also a key site for the ongoing reintroduction programs of two endangered species: the black rhinoceros ( Diceros bicornis ) and the African wild dog ( Lycaon pictus ) [ 30 ]. The dominant ethnic group in both villages is the Pare, with minority representation from the Sambaa and Maasai communities. The local economy is primarily based on subsistence agriculture, with major crops including maize, rice, cassava, sugarcane, bananas, and beans. The area experiences a tropical savannah climate characterized by distinct wet and dry seasons. The dry season spans from June to October, with average temperatures of 23.5°C and a mean monthly rainfall of approximately 106 mm. The wet season occurs from November to May, typically bringing higher rainfall and supporting crop cultivation. These ecological and socio-economic characteristics combined with the villages’ proximity to the park boundary make Mkonga Ijinyu and Kavambughu particularly vulnerable to human-elephant conflict, especially in the form of crop raiding. 2.2 Study population The study population included the local communities living adjacent to MKONAPA the key informants such as the local leaders, village agricultural officers, and representatives from the Tanzania Wildlife Authority (TAWA). 2.3 Sampling procedures and sample size The two study villages were purposively selected based on the high frequency of reported elephant-induced crop damage. A total of 162 households were selected through simple random sampling, comprising 92 from Mkonga Ijinyu and 70 from Kavambugu, out of a total of 643 and 202 households in each village, respectively. In addition, eight key informants were purposely sampled based on their positions, knowledge and experience. 2.4 Data collection methods Household questionnaire surveys, key informant interviews, and a literature review were employed to collect both quantitative and qualitative data on the spatial and socio-economic impacts of elephant-induced crop damage. The household questionnaire (Appendix 1) included both closed and open-ended questions. Respondents were asked about the types of crops damaged, frequency and timing of crop damage incidents, proximity of farms to the park boundary, and any mitigation measures implemented between 1st May 2023, and 30th April2024. All participants provided informed verbal consent before the interviews. Respondents were also assured of confidentiality and anonymity names were not recorded, and each questionnaire was assigned a numerical code. The questionnaire was administered in Swahili by a Zuhura Mrindoko Shabani (ZMS) and assisted by a trained field assistant to ensure clear communication and reduce response bias. Prior to the interviews, all participants were informed of the purpose of the study and were encouraged to seek clarification on any question they did not understand. Secondary data were obtained from official village records, reports from the District Game Office, and published literature on crop damage and human-elephant conflict in northern Tanzania. 2.3 Data analysis All quantitative data were analyzed using IBM SPSS Statistics Version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were first computed to summarize demographic characteristics of respondents and general trends in crop damage incidents. Frequency distributions and percentages were used to describe categorical variables, such as damaged crops, distance of farms from the park boundary, and timing of crop damage events. To examine the association between categorical variables and the incidence of crop damage, Pearson’s Chi-square (χ²) test was used. This test assessed whether variables such as village of residence, farm distance from the park boundary ( 5 km), season (wet vs. dry), immigration status (resident vs. non-resident), and shared water sources (yes/no) had a statistically significant influence on crop damage occurrence. Analysis of variance (ANOVA) was conducted to compare the mean area of crop damage between groups defined by the above independent variables. Prior to ANOVA, Levene’s Test for Homogeneity of Variance was performed to confirm that assumptions for parametric testing were met. Whereas assumptions were violated, non-parametric alternatives were considered. In addition, a Generalized Linear Model (GLM) was used to identify predictors of the area of crop damage (in hectares). The GLM included six independent variables: farm distance from the park boundary, level of education and age of the respondent. Model selection was based on Akaike’s Information Criterion (AIC), and statistical significance was set at P < 0.05. Qualitative data obtained through key informant interviews was transcribed, translated where necessary, and thematically analyzed. Key themes were identified relating to crop raiding patterns, perceived drivers of conflict, and local mitigation strategies. These qualitative insights were used to complement the quantitative findings and provide a broader contextual understanding of human-elephant interactions in the study area. 3. Results 3.1 Socio-demographic characteristics of respondents A total of 162 respondents participated in the survey. Of these, 58% were male while females accounted for 42.0%. Most respondents (54.3%) fell within the age range of 31–45 years followed by those aged 46–60 years (27.8%), 18–30 years (11.7%), and those above 60 years (6.2%). In terms of education levels, 67.9% of respondents had attained primary school education, 29.0% had secondary education, and only 3.1% had post-secondary education. Most respondents (88.3%) were permanent residents of the area, while 11.7% were immigrants. Most respondents (53.7%) were engaged in crop farming followed by agro-pastoralism (32.1%), small businesses (11.7%), and other occupations including teaching and transportation (2.5%). 3.2 Types of crops damaged by elephants Respondents reported that elephants damaged a wide range of crops. The most frequently affected crops were maize (88.3%), beans (76.5%), and sunflower (64.2%). Additional crops reported as damaged included rice (51.2%), sugarcane (38.3%), cassava (36.4%), pumpkin (29.6%), tomatoes (25.9%), cabbage (22.2%), and cotton (17.3%). In general, a higher frequency of crop damage was reported in Mkonga Ijinyu than in Kavambugu across nearly all crop types (Fig. 2 ). 3.3 Temporal pattern of crop damage Most respondents (96.9%, n = 162) reported that crop damage by elephants occurred seasonally, while a small minority (3.1%) indicated that such damage occurred on a daily basis in the village. 3.3.1 Time of the day Nearly all respondents (98.8%) indicated that crop damage incidents occurred predominantly at night. Most crop raiding events were seasonal (96.9%), although a few cases of daily damage (3.1%) were reported. Respondents observed an increasing trend in crop damage over the years, with 98.8% noting that annual incidents had risen to over 10 per year. Environmental factors such as land-use change (58%) and declining food and water availability within the park (42%) were cited as the primary drivers of this increase. 3.3.2 Seasonality of crop damage Respondents reported that crop damage was more severe during the harvest season (65.4%) compared to the growing season (34.6%). Mkonga Ijinyu experienced more crop damage during the dry season, while Kavambughu was more affected during the rainy season (Fig. 3 ). Damage was reported to peak between April and July, decrease significantly in August and September, and rise again in October (Figs. 3 and 4 ). 3.4 Spatial patterns of crop damage 3.4.1 Distance from the park boundary A statistically significant difference was found in the average area of crop damage (in acres) based on the distance of farms from the park boundary (F = 52.98, p < 0.001). Farmers with fields located less than 1 km from the park experienced the highest mean damage (mean = 3.32 acres, SD = 2.31, N = 31), followed by those with fields 1–5 km away (mean = 1.82 acres, SD = 0.64, N = 60). The least damage was reported by farmers whose fields were more than 5 km from the park boundary (mean = 0.87 acres, SD = 0.42, N = 71). Most respondents (59.9%, n = 162) reported that crop damage occurred primarily in agricultural fields near the park boundary, followed by areas close to water sources (30.9%) and the outskirts of the village (5.6%). Only a small proportion (3.6%) indicated that damage occurred near village centers. There was a statistically significant difference among age groups in reporting areas most prone to Human-Elephant Conflict (HEC) (χ² = 38.64, df = 9, p < 0.001). The highest proportion of respondents who identified agricultural fields as the most affected areas were in the 46–60 age group (66.7%, n = 45), followed by those aged 31–45 years (58.0%, n = 88), 18–30 years (57.9%, n = 19), and respondents over 60 years of age (50.0%, n = 10). There was a statistically significant difference across education levels in identifying areas most prone to Human-Elephant Conflict (HEC) (χ² = 16.34, df = 6, p < 0.012). Respondents with primary education reported the highest proportion of crop damage occurring in agricultural fields (69.1%, n = 110), followed by those with college or university education (60.0%, n = 5), and those with secondary education (38.3%, n = 47). A Generalized Linear Regression Model (GLM) was applied to examine important factors in explaining the observed variations in reporting magnitude of crop damage (acres) whereby mean damage as the explanatory response variable and distance, age, education level as predictors. The model indicated that only distance was a statistically significant predictor, accounting for 23.6% of the variation in crop damage by elephants (Table 1 ). Table 1 Crop damage by elephants as explanatory variable versus three predictor variables distance, age and level of education of the respondent. B = Beta coefficient; SE = Standard error; χ² = Wald Chi-square; df = Degrees of freedom; P = p-value. Variable Category B SE χ² df P (Intercept) 0.88 0.56 2.47 1 0.116 Distance < 1 km 2.51 0.25 102.17 1 < 0.001 1–5 km 0.91 0.20 20.55 1 5 km 0a . . . . Age 18–30 years 0.57 0.43 1.76 1 0.185 31–45 years 0.03 0.38 0.01 1 0.937 46–60 years 0.04 0.38 0.01 1 0.915 > 60 years 0a . . . . Level of education Primary 0 0.50 0 1 0.999 Secondary -0.36 0.52 0.49 1 0.483 College or University 0a . . . . (Scale) 1.163b 0.13 Dependent Variable: Mean crop damage Model: (Intercept), distance, age, level of education a.Set to zero because this parameter is redundant. b.Maximum likelihood estimate. 3.5 Extent of crop damage in acres Data obtained from the District Game Officer indicated that both Mkonga Ijinyu and Kavambugu villages experienced a general increase in elephant-induced crop damage between 2019 and 2023. Over the five-year period, Mkonga Ijinyu reported a total of 397 acres of crop damage, averaging approximately 99.25 acres annually. In contrast, Kavambugu experienced a total of 925 acres of damage across four years of recorded data, with an annual average of 231.25 acres. The highest single-year damage occurred in Kavambugu in 2023, with 527 acres affected (Table 2 ). Table 2 Crop damage in acres in Mkonga Ijinyu and Kavambughu from 2019 to 2023 Year Village Crop damage (acres) Animal species 2019 Mkonga Ijinyu 109 Elephant 2020 Mkonga Ijinyu 108 Elephant Kavambughu 45 Elephant 2021 Mkonga Ijinyu 119 Elephant Kavambughu 289 Elephant 2022 Kavambughu 64 Elephant 2023 Mkonga Ijinyu 61 Elephant Kavambughu 527 Elephant There was a statistically significant difference in mean crop damage (in acres) between the two villages (F = 53.22, df = 1, p < 0.001), with respondents from Mkonga Ijinyu reporting higher average damage (mean = 2.31 acres, SD = 1.61, N = 92) compared to those from Kavambughu (mean = 0.87 acres, SD = 0.42, N = 70). The majority of respondents (77.2%, n = 162) reported crop damage affecting 1–3 acres of farmland, followed by 4–7 acres (14.8%) and 8–11 acres (8.0%). A few respondents indicated crop losses exceeding 12 acres. 3.6 Socio-economic impact of crop damage On average, one acre of maize yields 12 to 13 sacks per household, with each 100 kg sack valued at approximately 80,000 TZS. In the study area, elephants can destroy up to one acre of crops in a single night, leading to annual crop losses ranging from 51–100% of the total harvest. Elephant-induced crop damage was reported to have significant effects on both livelihoods and children's education in the villages of Kavambugu and Mkonga Ijinyu. Of those reporting income loss (n = 110), 42.7% were from Kavambugu and 57.3% from Mkonga Ijinyu. Similarly, among respondents who reported food insecurity (n = 52), 44.2% were from Kavambugu and 55.8% from Mkonga Ijinyu. Furthermore, all respondents (n = 162) from both villages indicated that crop damage contributed to reduced school attendance among children in the affected communities. 3.7 HEC mitigation methods The most commonly employed mitigation strategy was night guarding, reported by 53.7% of respondents. This was followed by the use of combined deterrent methods such as watchtowers, fire, noise, and chili fences reported by 40.1% of respondents. Only 6.2% of respondents indicated the use of beehive fences as a mitigation approach. 4 Discussion 4.1 Socio-demographic characteristics of respondents The dominance of adult male respondents, most of whom were engaged in farming or agro-pastoralism with limited formal education, reflects a population highly dependent on natural resources for their livelihoods. Similar demographic patterns have been observed in other HEC-affected areas in Tanzania and sub-Saharan Africa, where land-dependent, low-income households are particularly vulnerable to wildlife incursions [ 12 , 14 ]. The concentration of respondents in the 31–60 years old age range suggests that many have accumulated experience with crop production and wildlife interactions, making their perceptions and observations particularly relevant for informing local conflict mitigation strategies. Limited education where nearly 68% had only primary education may hinder understanding of wildlife related laws and advanced deterrent techniques. As highlighted by [ 1 ], such socio-demographic variables significantly influence perceptions, tolerance thresholds, and participation in conservation programs. 4.2 Types and patterns of crop damage The range of crops damaged were predominantly maize, beans, and sunflowers demonstrates elephants’ strong preference for energy-rich, easily digestible agricultural produce. Maize ( Zea mays ) in particular, reported as the most affected crop (88.3%), has been consistently identified in numerous HEC studies due to its high sugar and starch content, which makes it more palatable than wild forage [ 8 , 28 , 31 ]. Beans and sunflowers, which are cultivated for both household consumption and cash income, similarly provide high caloric value, intensifying their vulnerability to elephant raids. The inclusion of other crops such as rice, sugarcane, cassava, and pumpkins in the damage profile reflects elephants’ generalist foraging behavior and opportunistic feeding patterns, particularly in fragmented landscapes where wild resources are limited [ 5 , 11 ]. This diversity of crop damage also suggests a wide-ranging impact across farm types and household economies. Importantly, the variation in crop types damaged between Mkonga Ijinyu and Kavambugu may be attributed to differences in cropping patterns, proximity to the park, and elephant movement corridors, a dynamic also observed in conflict-prone regions around Ruaha and Serengeti National Parks [ 26 , 32 , 33 ]. Moreover, the destruction of cash crops such as sunflower and sugarcane indicates the economic implications extend beyond subsistence food loss to broader income insecurity, a finding echoed in studies from India and Nepal [ 15 , 18 ]. These findings highlight the necessity of crop-specific conflict mitigation strategies and underscore the importance of mapping elephant crop preferences at the landscape level. 4.3 Temporal dynamics of crop damage The temporal distribution of crop raiding incidents in this study strongly reflects the ecological and behavioral rhythms of elephants. The predominance of night-time raids (98.8%) is consistent with findings from Uganda and Kenya, where elephants engage in nocturnal foraging to avoid human detection and confrontation [ 8 , 11 ]. This nocturnal behavior poses significant challenges for mitigation, as it requires sustained human vigilance and increases the risk of injury during defense attempts. Seasonal trends, with heightened damage during harvest periods (April–July and October), reveal elephants’ acute sensitivity to crop availability and phenology. Between villages there was variation whereas Mkonga Ijinyu have year-round agriculture because they irrigate even during the dry season while Kavambughu only cultivate during the rainy season where they do not rely on irrigation. These months often coincide with late dry and early rainy seasons, when natural forage becomes scarce within protected areas like Mkomazi, prompting elephants to forage beyond park boundaries [ 10 , 19 ]. Furthermore, the marked seasonality of raids is indicative of learned behavior and adaptive foraging, where elephants synchronize their incursions with crop maturation phases [ 16 ]. This predictability offers an opportunity for preemptive deterrent deployment during critical windows. Respondents’ attribution of increased crop damage to habitat loss and declining resources inside the park reflects a broader ecological crisis where park boundaries no longer represent absolute ecological limits. This supports [ 4 ] argument that coexistence requires active management of interface zones and anticipatory strategies tied to seasonal cycles. 4.4 Spatial patterns of crop damage The spatial concentration of crop damage near the park boundary (< 1 km) provides further evidence of a well-documented trend of the spatial risk gradient decreases with distance from wildlife habitats [ 26 – 28 ]. Farmers within this zone reported the highest average damage (mean = 3.32 acres), reinforcing the need for buffer zone reinforcement, strategic land use planning, and early warning systems. This study also found significant differences in perceived risk based on age and education levels, with older and less formally educated respondents more likely to identify agricultural fields as primary conflict zones. This suggests that personal experience with elephant incursions and generational knowledge contributes to spatial awareness of risk. Such localized knowledge is essential for participatory mapping and spatial prioritization of mitigation efforts [ 6 ]. Additionally, elephants’ attraction to water sources near farms further illustrates the interplay between landscape configuration and HEC risk. Shared use of water points increases encounter probability and necessitates coordinated water management strategies, possibly including alternative water provisioning within the park or along designated elephant corridors [ 7 ]. The presence of crop damage on the village periphery, albeit less frequent, indicates occasional deep incursions, especially in years of severe drought or heightened elephant movement, reinforcing the need for village-level surveillance and response systems. 4.5 Magnitude and trends in crop damage Over the five-year period assessed (2019–2023), a clear escalation in crop damage was recorded, particularly in Kavambugu where damage surged from 45 acres in 2020 to 527 acres in 2023. This dramatic increase aligns with findings from [ 17 ], who observed that elephant populations rebounding from anti-poaching efforts increasingly expand into agricultural lands when corridor connectivity is limited or degraded. The rising damage levels are also indicative of elephants habituating farms as reliable food sources, a behavioral shift that exacerbates long-term conflict [ 13 ]. The higher average damage reported in Mkonga Ijinyu compared to Kavambugu may reflect spatial differences in land cover, proximity to preferred elephant paths, or the density of farms near the boundary hence emphasizing the need for localized conflict assessment, rather than blanket policy interventions. The report that 77.2% of households lost between 1–3 acres per incident is significant when compared with data on local harvest yields. For many smallholders, the loss of even a single acre represents a substantial portion of their annual food supply and income. These statistics reinforce calls by [ 12 ] and [ 2 ] for more rigorous damage monitoring and standardized reporting mechanisms to inform policy and compensation frameworks. 4.6 Socio-economic impacts of crop damage The socio-economic consequences reported loss of income, food insecurity, and disrupted education are consistent with the broader literature on the indirect and long-term effects of HEC on rural livelihoods [ 1 , 18 ]. The finding that all respondents (100%) cited reduced school attendance due to HEC highlights the deep social penetration of this ecological problem. Children are often kept from school to assist in night guarding or because families cannot afford school fees after losing crops, compounding intergenerational poverty. The destruction of food and cash crops simultaneously undermines nutritional security and income diversity, especially in households reliant on seasonal harvests for sustenance and local trade. These pressures can fuel resentment toward wildlife authorities and erode public support for conservation, particularly when compensation mechanisms are absent or perceived as unjust [ 2 , 23 , 25 ]. Moreover, the overlap between food insecurity and educational disruption suggests that HEC not only affects immediate economic outcomes but also undermines the long-term development capacity of entire communities. Addressing these impacts requires a holistic strategy that goes beyond reactive deterrence to incorporate education support, livelihood diversification, and resilience-building interventions. 4.7 Effectiveness of mitigation strategies The predominance of night guarding (53.7%) as the primary mitigation strategy illustrates both community commitment and the absence of accessible alternatives. However, night guarding is labor-intensive, exposes individuals to physical danger, and has diminishing effectiveness against habituated elephants [ 15 ]. The use of combined deterrents such as noise (air horn, drums etc), fire, and chili fences shows some community-level innovation, yet these methods often suffer from inconsistent implementation and limited technical support. Notably, only 6.2% of respondents reported using beehive fences, despite their proven efficacy in East African landscapes [ 13 ]. Low uptake may reflect high initial costs, limited training, or skepticism regarding effectiveness. This calls for targeted capacity-building programs and subsidies to promote adoption. Integrating modern tools (e.g., motion-sensor alarms, mobile-based alert systems) with traditional methods could improve efficiency, especially when coupled with community-based monitoring teams. These findings reflect broader concerns raised in the literature regarding the sustainability and scalability of current HEC mitigation strategies [ 6 , 14 ]. Moving forward, conflict mitigation must be embedded within integrated conservation frameworks that combine ecological science, local knowledge, and equitable benefit-sharing mechanisms [ 34 ]. 5. Conclusions This study reveals that elephant-induced crop damage in villages adjacent to MKONAPA is spatially clustered, temporally predictable, and socio-economically devastating. While the problem is escalating, current mitigation measures remain inadequate and unsustainable. To sustainably devise effective mitigation method for HEC mitigation in the MKONAPA we recommend including stakeholders from both Tsavo and Mkomazi and harmonization of policies in both sides. These findings point to the urgent need for evidence-based HEC management strategies that integrate landscape-level planning, provision of community conservation education on HEC mitigation methods, and innovative deterrents, aligned with Tanzania’s national biodiversity goals and the Kunming-Montreal Global Biodiversity Framework (CBD, 2022). Declarations Data availability statement Data supporting the findings of this study is available from the corresponding author on a reasonable request. Author’s contribution KMH, ZMS, RAS, SHM and TK conceptualized and designed the study. ZMS collected and analyzed the data. KMH re-analyzed data and wrote the article with the support of RAS, SHM and TK. Acknowledgement The authors sincerely thank the village leaders of all the two villages in the Same District where this study was conducted for their cooperation during our data collection. The authors also send their sincere thanks to the College of African Wildlife Management (CAWM) through Research, Publication and Consultancy Unit for the logistic support that allowed the preparation of this manuscript. Funding The study did not receive funding from a specific funding agency. Ethical statement Prior to data collection, research permit was obtained from the Research Committee from Research, Publication and consultancy Unit at the College of African Wildlife Management. The data collection procedures for respondent interviews adhered to the ethical standards of the College of African Wildlife Management and followed the principles outlined in the World Medical Association Declaration of Helsinki (WMA, 2013). Informed consent was obtained from all participants prior to their involvement in the survey. At the start of each interview, respondents were informed of their right to seek clarification at any point during the process. To maintain anonymity, participants’ names were not recorded, and each questionnaire was assigned a unique identification number. The study did not involve any human health-related issues. Additionally, verbal permission to conduct research in the selected study areas was obtained from the District Executive Director of Same District, the District Game Officer, and the Village Executive Officers of Kavambughu and Mkonga Ijinyu villages. Competing interests The authors declare that they have no competing interest. Consent to publish declaration Respondents were informed about the objectives of the study and the eventual publications of the information collected and were assured that their identities would remain undisclosed. References Kansky R, Kidd M, Knight AT. Meta-Analysis of Attitudes toward Damage-Causing Mammalian Wildlife. Conserv Biol. 2014;28(4):924–38. https://doi.org/10.1016/j.biocon.2014.09.008 . Pozo RA, Cusack JJ, Young JC. 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Diniz MF, Cushman SA, Machado RB, De Marco P, Júnior. Landscape connectivity modeling from the perspective of animal dispersal. Landscape Ecology, 2020. 35(1): pp. 41–58. https://doi.org/10.1007/s10980-019-00935-3 Chiyo PI, Cochrane EP, Naughton L, Basuta GI. Temporal patterns of crop raiding by elephants: a response to changes in forage quality or crop availability? Afr J Ecol. 2005;43(1):48–55. https://doi.org/10.1111/j.1365-2028.2005.00577.x . Chiyo PI, Cochrane EP. Population structure and behaviour of crop-raiding elephants in Kibale National Park, Uganda. Afr J Ecol. 2005;43(3):233–41. ://000232978200012. Hefty KL, Koprowski JL. Multiscale effects of habitat loss and degradation on occurrence and landscape connectivity of a threatened subspecies. Conserv Sci Pract. 2021;3(12):e547. https://doi.org/10.1111/csp2.547 . Mukeka JM, Ogutu JO, Kanga E, Røskaft E. Spatial and temporal dynamics of human–wildlife conflicts in the Kenya Greater Tsavo Ecosystem. Human-Wildlife Interact. 2020;14(2):255–72. https://www.jstor.org/stable/27316197 . Hariohay KM, Fyumagwa RD, Kideghesho JR, Røskaft E. Assessing crop and livestock losses along the Rungwa-Katavi Wildlife Corridor, South-Western Tanzania. Int J Biodivers Conserv. 2017;9(8):273–83. https://doi.org/10.5897/IJBC2017.1116 . Shaffer LJ, Khadka KK, Van Den Hoek J, Naithani KJ. Human-Elephant Conflict: A Review of Current Management Strategies and Future Directions. Frontiers in Ecology and Evolution, 2019. 6(2018). https://doi.org/10.3389/fevo.2018.00235 Seoraj-Pillai N, Pillay N. A Meta-Analysis of Human–Wildlife Conflict: South African and Global Perspectives. Sustainability. 2017;9(1):34. https://www.mdpi.com/2071-1050/9/1/34 . Chakraborty S, Paul N. Efficacy of different human-elephant conflict prevention and mitigation techniques practiced in West Bengal, India. Notulae Scientia Biologicae. 2021;13(3):11017. https://doi.org/10.15835/nsb13311017 . Thant ZM, May R, Røskaft E. Human–elephant coexistence challenges in Myanmar: An analysis of fatal elephant attacks on humans and elephant mortality. J Nat Conserv. 2022;69:126260. https://doi.org/10.1016/j.jnc.2022.126260 . Chase MJ, Schlossberg S, Griffin CR, Bouché PJC, Djene SW, Elkan PW, Ferreira S, Grossman F, Kohi EM, Landen K, Omondi P, Peltier A, Selier SAJ, Sutcliffe R. Continent-wide survey reveals massive decline in African savannah elephants. PeerJ. 2016;4:e2354. https://doi.org/10.7717/peerj.2354 . Barua M, Bhagwat SA, Jadhav S. The hidden dimensions of human-wildlife conflict: Health impacts, opportunity and transaction costs. Biol Conserv. 2013;157:309–16. https://doi.org/10.1016/j.biocon.2012.07.014 . Redmore L. Understanding human-elephant interactions across time is key to illuminate pathways toward coexistence. Ecol Soc. 2024;29(3). https://doi.org/10.5751/es-15343-290333 . Zhang H, Guo S, Ma L, Su K, Lobora A, Hou Y, Wen Y. Living with elephants: Analyzing commonalities and differences in human-elephant conflicts in China and Tanzania based on residents' perspectives. Global Ecol Conserv. 2024;53:e03034. https://doi.org/10.1016/j.gecco.2024.e03034 . TAWIRI, Aerial Wildlife Survey Report for the Selous-Mikumi Ecosystem, Tanzania . 2019, Tanzania Wildlife Research Institute: Arusha. TAWIRI. State of Elephants in Tanzania: National Update Report 2022 . 2022. Hariohay KM, Røskaft E. Wildlife induced damage to crops and livestock loss and how they affect human attitudes in the kwakuchinja wildlife corridor in northern Tanzania. Environ Nat Resour J. 2015;5(3). https://doi.org/10.5539/enrr.v5n3pxx . Kibira G. The Economic Value of Serengeti National Park in Tanzania and Implications for Adjacent Local Communities . 2021. Kegamba JJ. Assessment of conservation institutional frameworks and benefit-sharing mechanisms for local peoples in the Greater Serengeti Ecosystem, Tanzania. Charles Darwin University; 2024. Hariohay KM, Munuo WA, Røskaft E. Human–elephant interactions in areas surrounding the Rungwa, Kizigo, and Muhesi Game Reserves, central Tanzania. Oryx, 2019: pp. 1–9. https://doi.org/10.1017/S003060531800128X Hoare RE, Du JT, Toit. Coexistence between people and elephants in African savannas. Conserv Biol. 1999;13(3):633–9. ://000080720400020. Gillingham S, Lee PC. A preliminary assessment of perceived and actual patterns of wildlife crop damage in an area bordering the Selous Game Reserve. Tanzania Oryx. 2003;37:316–25. https://doi.org/10.1017/S0030605303000577 . CBD. Kunming-Montreal Global Biodiversity Framework. 2022; :[Available from: https://www.cbd.int/article/cop15-final-text-kunming-montreal-gbf-221222 Tanzania National Parks Authority. MKONAPA: General management plan (2021–2031). Tanzania National Parks Authority: Ausha, Tanzania; 2021. Gross EM, McRobb R, Gross J. Cultivating alternative crops reduces crop losses due to African elephants. J Pest Sci. 2016;89(2):497–506. https://doi.org/10.1007/s10340-015-0699-2 . Hampson K, McCabe JT, Estes A, Ogutu JO, Rentsch D, Craft M, Hemed CB, Ernest E, Hoare R, Kissui B. Living in the greater Serengeti ecosystem: human-wildlife conflict and coexistence. Serengeti IV: Sustaining biodiversity in a coupled humannatural system. Chicago, Illinois, USA: The University of Chicago Press; 2015. pp. 607–45. Hariohay KM, Gambay JG, Rskaft E. Attitudes of local leaders towards wildlife conservation in village areas in southern Ngorongoro Conservation Area, Karatu District, Tanzania. Int J Biodivers Conserv. 2020;12(3):227–39. Hariohay, K.M., M.A. Cletus, L.E. Happygod, H. Louis, K.J. Ramadhan, and E. and Røskaft, Can conservation-based incentives promote willingness of local communities to coexist with wildlife? A case of Burunge Wildlife Management Area, Northern Tanzania. Human Dimensions of Wildlife, 2024. 30(2): pp. 149–162. https://doi.org/10.1080/10871209.2024.2333550. 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02:20:33","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138732,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/261149b44ff9c3ff3e998802.html"},{"id":93539706,"identity":"17a3968f-997a-473b-892e-3a62e7f02540","added_by":"auto","created_at":"2025-10-15 02:20:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":469524,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area showing the location of Kavambughu and Mkonga Ijinyu villages adjacent to the MKONAPA\u003c/p\u003e","description":"","filename":"Fig.1Studyarea.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/8638d2d84497071cdf28f681.jpg"},{"id":93539704,"identity":"6dd83dba-1541-46ca-b9e2-6e1d3ddb655e","added_by":"auto","created_at":"2025-10-15 02:20:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60421,"visible":true,"origin":"","legend":"\u003cp\u003eType of crop damaged by elephants\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/5a92b2f865ee639327d2e983.png"},{"id":93541162,"identity":"9dc3367a-fc68-4dc5-a6f9-d8bea53fe04b","added_by":"auto","created_at":"2025-10-15 02:28:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38099,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonality of crop damage\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/86443f22db418ec5452fa15a.png"},{"id":93541163,"identity":"a39ffd18-bee2-4e76-943f-f06080351df7","added_by":"auto","created_at":"2025-10-15 02:28:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35719,"visible":true,"origin":"","legend":"\u003cp\u003eMonth of the year when the problem of crop damage is more severe\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/94041fa2cfb805cab19f2ad4.png"},{"id":98946200,"identity":"2fcf17bc-e016-42d9-95ff-7d5bcf1cffb2","added_by":"auto","created_at":"2025-12-24 12:25:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1571493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7093604/v1/4ce97645-b2f1-4083-b27f-d4f16fe36c14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elephant crop raiding in northern Tanzania: Spatio-temporal trends and damage assessment in villages adjacent to Mkomazi National Park","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHuman-wildlife conflict (HWC) represents one of the most pervasive challenges in contemporary conservation, undermining both biodiversity protection and human well-being, particularly in rural, biodiversity-rich areas where livelihoods depend heavily on natural resources [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The issue is especially pronounced in tropical and subtropical landscapes, where rapid human population growth, agricultural expansion, and climate-induced changes intensify competition for land and water between humans and wildlife [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Elephants, due to their ecological needs and behavioral flexibility, are at the forefront of this conflict, generating disproportionate damage in comparison to other wildlife species [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eElephants are long-lived, wide-ranging megaherbivores with high cognitive capacity and social complexity. As ecosystem engineers, they contribute significantly to landscape structuring and seed dispersal. However, these same traits combined with increasing habitat fragmentation also enable them to exploit anthropogenic landscapes, particularly during periods of resource scarcity [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Their foraging behavior often brings them into direct conflict with smallholder farmers, leading to extensive crop damage, infrastructure destruction, human injury or death, and in some cases retaliatory killings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As a result, human-elephant conflict (HEC) is now one of the most urgent threats to the long-term viability of elephant populations and community-based conservation programs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, HEC is reported in over 50 countries across Africa and Asia, with at least 37 African countries facing elephant-related damage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In South Asia, India alone accounts for approximately 60% of the global Asian elephant (\u003cem\u003eElephas maximus\u003c/em\u003e) population, and reports over 500 human deaths annually due to elephant encounters, alongside large-scale crop, and property losses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In sub-Saharan Africa, the return of elephant populations in several countries, owing to improved anti-poaching measures and international trade bans, has inadvertently led to a resurgence of conflict near protected areas and migratory corridors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While this recovery is ecologically encouraging, it is socially and politically fraught, particularly where rural communities experience repeated crop failures and inadequate mitigation or compensation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Tanzania home to one of the largest elephant populations in East Africa, the human-elephant interface has grown increasingly volatile in recent decades [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. From 2009 to 2015, Tanzania experienced a drastic population decline due to poaching, but through strong enforcement and habitat protection efforts, elephant numbers increased from approximately 43,000 in 2014 to around 60,000 by 2021 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Unfortunately, this conservation success has not been matched by robust human-elephant coexistence strategies. Many rural communities bordering protected areas such as those adjacent to Serengeti, Ruaha, and Mkomazi National Park (MKONAPA) have reported increasing crop losses, food insecurity, and negative attitudes toward conservation authorities [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMKONAPA is located in northeastern Tanzania, exemplifies this growing challenge. Its proximity to expanding settlements and farmland in Same District\u0026rsquo;s Mkonga Ijinyu and Kavambughu villages has created a conflict hotspot. These communities, located within a 1\u0026ndash;14 km radius of the park, depend largely on rainfed subsistence agriculture and regularly experience elephant incursions, particularly during the dry season and harvest periods [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Yet, limited empirical evidence exists on the scale, pattern, and socio-economic impacts of HEC in this specific landscape.\u003c/p\u003e\u003cp\u003eUnderstanding the spatial and temporal dimensions of elephant-induced crop damage is essential for designing effective and context-specific mitigation strategies. Previous studies in other Tanzanian ecosystems have highlighted the significance of factors such as distance from park boundaries, land-use type, seasonality, and community perceptions in influencing both conflict severity and local responses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, such integrated assessments are lacking for Mkomazi, a region that has received relatively little research attention despite growing reports of elephant activity and crop damage.\u003c/p\u003e\u003cp\u003eThis study aims to fill this critical gap by systematically analyzing the extent, temporal trends, and socio-economic consequences of elephant-induced crop damage in two villages bordering MKONAPA. Specifically, it examines how the proximity to the park, seasonal variations, and household characteristics influence crop damage risk, and how local communities perceive and respond to this challenge. The findings are intended to inform targeted mitigation approaches, support evidence-based conservation planning, and contribute to broader efforts to balance biodiversity conservation with rural development, especially within the framework of the Kunming-Montreal Global Biodiversity Framework and the Sustainable Development Goals (SDGs 11, 13 and 15), which call for inclusive and equitable conservation strategies that recognize human needs and rights [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area description\u003c/h2\u003e\u003cp\u003eData were collected from two villages Mkonga Ijinyu and Kavambugu located adjacent to the eastern boundary of MKONAPA in Same District, Kilimanjaro Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). MKONAPA (MNP) is located in northeastern Tanzania, lying between latitudes 3\u0026deg;47\u0026prime; to 4\u0026deg;33\u0026prime;S and longitudes 37\u0026deg;45\u0026prime; to 38\u0026deg;45\u0026prime;E. The Mkomazi park covers an area of approximately 3,245 km\u0026sup2; and is recognized for its diverse ecosystems, including dry savannahs, acacia woodlands, and seasonal river systems. MKONAPA is home to over 400 bird species and more than 90 mammalian species, including populations of African elephants (\u003cem\u003eLoxodonta africana\u003c/em\u003e), giraffes (\u003cem\u003eGiraffa camelopardalis\u003c/em\u003e), zebras (\u003cem\u003eEquus quagga\u003c/em\u003e), and predators such as lions (\u003cem\u003ePanthera leo\u003c/em\u003e) and leopards (\u003cem\u003ePanthera pardus\u003c/em\u003e). The park is also a key site for the ongoing reintroduction programs of two endangered species: the black rhinoceros (\u003cem\u003eDiceros bicornis\u003c/em\u003e) and the African wild dog (\u003cem\u003eLycaon pictus\u003c/em\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe dominant ethnic group in both villages is the Pare, with minority representation from the Sambaa and Maasai communities. The local economy is primarily based on subsistence agriculture, with major crops including maize, rice, cassava, sugarcane, bananas, and beans. The area experiences a tropical savannah climate characterized by distinct wet and dry seasons. The dry season spans from June to October, with average temperatures of 23.5\u0026deg;C and a mean monthly rainfall of approximately 106 mm. The wet season occurs from November to May, typically bringing higher rainfall and supporting crop cultivation. These ecological and socio-economic characteristics combined with the villages\u0026rsquo; proximity to the park boundary make Mkonga Ijinyu and Kavambughu particularly vulnerable to human-elephant conflict, especially in the form of crop raiding.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eThe study population included the local communities living adjacent to MKONAPA the key informants such as the local leaders, village agricultural officers, and representatives from the Tanzania Wildlife Authority (TAWA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.3 Sampling procedures and sample size\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe two study villages were purposively selected based on the high frequency of reported elephant-induced crop damage. A total of 162 households were selected through simple random sampling, comprising 92 from Mkonga Ijinyu and 70 from Kavambugu, out of a total of 643 and 202 households in each village, respectively. In addition, eight key informants were purposely sampled based on their positions, knowledge and experience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data collection methods\u003c/h2\u003e\u003cp\u003eHousehold questionnaire surveys, key informant interviews, and a literature review were employed to collect both quantitative and qualitative data on the spatial and socio-economic impacts of elephant-induced crop damage.\u003c/p\u003e\u003cp\u003eThe household questionnaire (Appendix 1) included both closed and open-ended questions. Respondents were asked about the types of crops damaged, frequency and timing of crop damage incidents, proximity of farms to the park boundary, and any mitigation measures implemented between 1st May 2023, and 30th April2024. All participants provided informed verbal consent before the interviews. Respondents were also assured of confidentiality and anonymity names were not recorded, and each questionnaire was assigned a numerical code.\u003c/p\u003e\u003cp\u003eThe questionnaire was administered in Swahili by a Zuhura Mrindoko Shabani (ZMS) and assisted by a trained field assistant to ensure clear communication and reduce response bias. Prior to the interviews, all participants were informed of the purpose of the study and were encouraged to seek clarification on any question they did not understand. Secondary data were obtained from official village records, reports from the District Game Office, and published literature on crop damage and human-elephant conflict in northern Tanzania.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\u003cp\u003eAll quantitative data were analyzed using IBM SPSS Statistics Version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were first computed to summarize demographic characteristics of respondents and general trends in crop damage incidents. Frequency distributions and percentages were used to describe categorical variables, such as damaged crops, distance of farms from the park boundary, and timing of crop damage events.\u003c/p\u003e\u003cp\u003eTo examine the association between categorical variables and the incidence of crop damage, Pearson\u0026rsquo;s Chi-square (χ\u0026sup2;) test was used. This test assessed whether variables such as village of residence, farm distance from the park boundary (\u0026lt;\u0026thinsp;1 km, 1\u0026ndash;5 km, \u0026gt;\u0026thinsp;5 km), season (wet vs. dry), immigration status (resident vs. non-resident), and shared water sources (yes/no) had a statistically significant influence on crop damage occurrence.\u003c/p\u003e\u003cp\u003eAnalysis of variance (ANOVA) was conducted to compare the mean area of crop damage between groups defined by the above independent variables. Prior to ANOVA, Levene\u0026rsquo;s Test for Homogeneity of Variance was performed to confirm that assumptions for parametric testing were met. Whereas assumptions were violated, non-parametric alternatives were considered.\u003c/p\u003e\u003cp\u003eIn addition, a Generalized Linear Model (GLM) was used to identify predictors of the area of crop damage (in hectares). The GLM included six independent variables: farm distance from the park boundary, level of education and age of the respondent. Model selection was based on Akaike\u0026rsquo;s Information Criterion (AIC), and statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eQualitative data obtained through key informant interviews was transcribed, translated where necessary, and thematically analyzed. Key themes were identified relating to crop raiding patterns, perceived drivers of conflict, and local mitigation strategies. These qualitative insights were used to complement the quantitative findings and provide a broader contextual understanding of human-elephant interactions in the study area.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Socio-demographic characteristics of respondents\u003c/h2\u003e\u003cp\u003eA total of 162 respondents participated in the survey. Of these, 58% were male while females accounted for 42.0%. Most respondents (54.3%) fell within the age range of 31\u0026ndash;45 years followed by those aged 46\u0026ndash;60 years (27.8%), 18\u0026ndash;30 years (11.7%), and those above 60 years (6.2%).\u003c/p\u003e\u003cp\u003eIn terms of education levels, 67.9% of respondents had attained primary school education, 29.0% had secondary education, and only 3.1% had post-secondary education. Most respondents (88.3%) were permanent residents of the area, while 11.7% were immigrants. Most respondents (53.7%) were engaged in crop farming followed by agro-pastoralism (32.1%), small businesses (11.7%), and other occupations including teaching and transportation (2.5%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Types of crops damaged by elephants\u003c/h2\u003e\u003cp\u003eRespondents reported that elephants damaged a wide range of crops. The most frequently affected crops were maize (88.3%), beans (76.5%), and sunflower (64.2%). Additional crops reported as damaged included rice (51.2%), sugarcane (38.3%), cassava (36.4%), pumpkin (29.6%), tomatoes (25.9%), cabbage (22.2%), and cotton (17.3%). In general, a higher frequency of crop damage was reported in Mkonga Ijinyu than in Kavambugu across nearly all crop types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Temporal pattern of crop damage\u003c/h2\u003e\u003cp\u003eMost respondents (96.9%, n\u0026thinsp;=\u0026thinsp;162) reported that crop damage by elephants occurred seasonally, while a small minority (3.1%) indicated that such damage occurred on a daily basis in the village.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Time of the day\u003c/h2\u003e\u003cp\u003eNearly all respondents (98.8%) indicated that crop damage incidents occurred predominantly at night. Most crop raiding events were seasonal (96.9%), although a few cases of daily damage (3.1%) were reported. Respondents observed an increasing trend in crop damage over the years, with 98.8% noting that annual incidents had risen to over 10 per year. Environmental factors such as land-use change (58%) and declining food and water availability within the park (42%) were cited as the primary drivers of this increase.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Seasonality of crop damage\u003c/h2\u003e\u003cp\u003eRespondents reported that crop damage was more severe during the harvest season (65.4%) compared to the growing season (34.6%). Mkonga Ijinyu experienced more crop damage during the dry season, while Kavambughu was more affected during the rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Damage was reported to peak between April and July, decrease significantly in August and September, and rise again in October (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Spatial patterns of crop damage\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Distance from the park boundary\u003c/h2\u003e\u003cp\u003eA statistically significant difference was found in the average area of crop damage (in acres) based on the distance of farms from the park boundary (F\u0026thinsp;=\u0026thinsp;52.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Farmers with fields located less than 1 km from the park experienced the highest mean damage (mean\u0026thinsp;=\u0026thinsp;3.32 acres, SD\u0026thinsp;=\u0026thinsp;2.31, N\u0026thinsp;=\u0026thinsp;31), followed by those with fields 1\u0026ndash;5 km away (mean\u0026thinsp;=\u0026thinsp;1.82 acres, SD\u0026thinsp;=\u0026thinsp;0.64, N\u0026thinsp;=\u0026thinsp;60). The least damage was reported by farmers whose fields were more than 5 km from the park boundary (mean\u0026thinsp;=\u0026thinsp;0.87 acres, SD\u0026thinsp;=\u0026thinsp;0.42, N\u0026thinsp;=\u0026thinsp;71).\u003c/p\u003e\u003cp\u003eMost respondents (59.9%, n\u0026thinsp;=\u0026thinsp;162) reported that crop damage occurred primarily in agricultural fields near the park boundary, followed by areas close to water sources (30.9%) and the outskirts of the village (5.6%). Only a small proportion (3.6%) indicated that damage occurred near village centers.\u003c/p\u003e\u003cp\u003eThere was a statistically significant difference among age groups in reporting areas most prone to Human-Elephant Conflict (HEC) (χ\u0026sup2; = 38.64, df\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest proportion of respondents who identified agricultural fields as the most affected areas were in the 46\u0026ndash;60 age group (66.7%, n\u0026thinsp;=\u0026thinsp;45), followed by those aged 31\u0026ndash;45 years (58.0%, n\u0026thinsp;=\u0026thinsp;88), 18\u0026ndash;30 years (57.9%, n\u0026thinsp;=\u0026thinsp;19), and respondents over 60 years of age (50.0%, n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\u003cp\u003eThere was a statistically significant difference across education levels in identifying areas most prone to Human-Elephant Conflict (HEC) (χ\u0026sup2; = 16.34, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.012). Respondents with primary education reported the highest proportion of crop damage occurring in agricultural fields (69.1%, n\u0026thinsp;=\u0026thinsp;110), followed by those with college or university education (60.0%, n\u0026thinsp;=\u0026thinsp;5), and those with secondary education (38.3%, n\u0026thinsp;=\u0026thinsp;47).\u003c/p\u003e\u003cp\u003eA Generalized Linear Regression Model (GLM) was applied to examine important factors in explaining the observed variations in reporting magnitude of crop damage (acres) whereby mean damage as the explanatory response variable and distance, age, education level as predictors. The model indicated that only distance was a statistically significant predictor, accounting for 23.6% of the variation in crop damage by elephants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCrop damage by elephants as explanatory variable versus three predictor variables distance, age and level of education of the respondent. B\u0026thinsp;=\u0026thinsp;Beta coefficient; SE\u0026thinsp;=\u0026thinsp;Standard error; χ\u0026sup2; = Wald Chi-square; df\u0026thinsp;=\u0026thinsp;Degrees of freedom; P\u0026thinsp;=\u0026thinsp;p-value.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1 km\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;5 km\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 km\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege or University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.163b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eDependent Variable: Mean crop damage\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel: (Intercept), distance, age, level of education\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ea.Set to zero because this parameter is redundant.\u003c/p\u003e\u003cp\u003eb.Maximum likelihood estimate.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Extent of crop damage in acres\u003c/h2\u003e\u003cp\u003eData obtained from the District Game Officer indicated that both Mkonga Ijinyu and Kavambugu villages experienced a general increase in elephant-induced crop damage between 2019 and 2023. Over the five-year period, Mkonga Ijinyu reported a total of 397 acres of crop damage, averaging approximately 99.25 acres annually. In contrast, Kavambugu experienced a total of 925 acres of damage across four years of recorded data, with an annual average of 231.25 acres. The highest single-year damage occurred in Kavambugu in 2023, with 527 acres affected (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCrop damage in acres in Mkonga Ijinyu and Kavambughu from 2019 to 2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrop damage (acres)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnimal species\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMkonga Ijinyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMkonga Ijinyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKavambughu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMkonga Ijinyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKavambughu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKavambughu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMkonga Ijinyu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKavambughu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElephant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere was a statistically significant difference in mean crop damage (in acres) between the two villages (F\u0026thinsp;=\u0026thinsp;53.22, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with respondents from Mkonga Ijinyu reporting higher average damage (mean\u0026thinsp;=\u0026thinsp;2.31 acres, SD\u0026thinsp;=\u0026thinsp;1.61, N\u0026thinsp;=\u0026thinsp;92) compared to those from Kavambughu (mean\u0026thinsp;=\u0026thinsp;0.87 acres, SD\u0026thinsp;=\u0026thinsp;0.42, N\u0026thinsp;=\u0026thinsp;70). The majority of respondents (77.2%, n\u0026thinsp;=\u0026thinsp;162) reported crop damage affecting 1\u0026ndash;3 acres of farmland, followed by 4\u0026ndash;7 acres (14.8%) and 8\u0026ndash;11 acres (8.0%). A few respondents indicated crop losses exceeding 12 acres.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Socio-economic impact of crop damage\u003c/h2\u003e\u003cp\u003eOn average, one acre of maize yields 12 to 13 sacks per household, with each 100 kg sack valued at approximately 80,000 TZS. In the study area, elephants can destroy up to one acre of crops in a single night, leading to annual crop losses ranging from 51\u0026ndash;100% of the total harvest.\u003c/p\u003e\u003cp\u003eElephant-induced crop damage was reported to have significant effects on both livelihoods and children's education in the villages of Kavambugu and Mkonga Ijinyu. Of those reporting income loss (n\u0026thinsp;=\u0026thinsp;110), 42.7% were from Kavambugu and 57.3% from Mkonga Ijinyu. Similarly, among respondents who reported food insecurity (n\u0026thinsp;=\u0026thinsp;52), 44.2% were from Kavambugu and 55.8% from Mkonga Ijinyu. Furthermore, all respondents (n\u0026thinsp;=\u0026thinsp;162) from both villages indicated that crop damage contributed to reduced school attendance among children in the affected communities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.7 HEC mitigation methods\u003c/h2\u003e\u003cp\u003eThe most commonly employed mitigation strategy was night guarding, reported by 53.7% of respondents. This was followed by the use of combined deterrent methods such as watchtowers, fire, noise, and chili fences reported by 40.1% of respondents. Only 6.2% of respondents indicated the use of beehive fences as a mitigation approach.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Socio-demographic characteristics of respondents\u003c/h2\u003e\u003cp\u003eThe dominance of adult male respondents, most of whom were engaged in farming or agro-pastoralism with limited formal education, reflects a population highly dependent on natural resources for their livelihoods. Similar demographic patterns have been observed in other HEC-affected areas in Tanzania and sub-Saharan Africa, where land-dependent, low-income households are particularly vulnerable to wildlife incursions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe concentration of respondents in the 31\u0026ndash;60 years old age range suggests that many have accumulated experience with crop production and wildlife interactions, making their perceptions and observations particularly relevant for informing local conflict mitigation strategies. Limited education where nearly 68% had only primary education may hinder understanding of wildlife related laws and advanced deterrent techniques. As highlighted by [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], such socio-demographic variables significantly influence perceptions, tolerance thresholds, and participation in conservation programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Types and patterns of crop damage\u003c/h2\u003e\u003cp\u003eThe range of crops damaged were predominantly maize, beans, and sunflowers demonstrates elephants\u0026rsquo; strong preference for energy-rich, easily digestible agricultural produce. Maize (\u003cem\u003eZea mays\u003c/em\u003e) in particular, reported as the most affected crop (88.3%), has been consistently identified in numerous HEC studies due to its high sugar and starch content, which makes it more palatable than wild forage [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Beans and sunflowers, which are cultivated for both household consumption and cash income, similarly provide high caloric value, intensifying their vulnerability to elephant raids.\u003c/p\u003e\u003cp\u003eThe inclusion of other crops such as rice, sugarcane, cassava, and pumpkins in the damage profile reflects elephants\u0026rsquo; generalist foraging behavior and opportunistic feeding patterns, particularly in fragmented landscapes where wild resources are limited [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This diversity of crop damage also suggests a wide-ranging impact across farm types and household economies. Importantly, the variation in crop types damaged between Mkonga Ijinyu and Kavambugu may be attributed to differences in cropping patterns, proximity to the park, and elephant movement corridors, a dynamic also observed in conflict-prone regions around Ruaha and Serengeti National Parks [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, the destruction of cash crops such as sunflower and sugarcane indicates the economic implications extend beyond subsistence food loss to broader income insecurity, a finding echoed in studies from India and Nepal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings highlight the necessity of crop-specific conflict mitigation strategies and underscore the importance of mapping elephant crop preferences at the landscape level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Temporal dynamics of crop damage\u003c/h2\u003e\u003cp\u003eThe temporal distribution of crop raiding incidents in this study strongly reflects the ecological and behavioral rhythms of elephants. The predominance of night-time raids (98.8%) is consistent with findings from Uganda and Kenya, where elephants engage in nocturnal foraging to avoid human detection and confrontation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This nocturnal behavior poses significant challenges for mitigation, as it requires sustained human vigilance and increases the risk of injury during defense attempts.\u003c/p\u003e\u003cp\u003eSeasonal trends, with heightened damage during harvest periods (April\u0026ndash;July and October), reveal elephants\u0026rsquo; acute sensitivity to crop availability and phenology. Between villages there was variation whereas Mkonga Ijinyu have year-round agriculture because they irrigate even during the dry season while Kavambughu only cultivate during the rainy season where they do not rely on irrigation. These months often coincide with late dry and early rainy seasons, when natural forage becomes scarce within protected areas like Mkomazi, prompting elephants to forage beyond park boundaries [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the marked seasonality of raids is indicative of learned behavior and adaptive foraging, where elephants synchronize their incursions with crop maturation phases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This predictability offers an opportunity for preemptive deterrent deployment during critical windows.\u003c/p\u003e\u003cp\u003eRespondents\u0026rsquo; attribution of increased crop damage to habitat loss and declining resources inside the park reflects a broader ecological crisis where park boundaries no longer represent absolute ecological limits. This supports [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e4\u003c/span\u003e] argument that coexistence requires active management of interface zones and anticipatory strategies tied to seasonal cycles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Spatial patterns of crop damage\u003c/h2\u003e\u003cp\u003eThe spatial concentration of crop damage near the park boundary (\u0026lt;\u0026thinsp;1 km) provides further evidence of a well-documented trend of the spatial risk gradient decreases with distance from wildlife habitats [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Farmers within this zone reported the highest average damage (mean\u0026thinsp;=\u0026thinsp;3.32 acres), reinforcing the need for buffer zone reinforcement, strategic land use planning, and early warning systems.\u003c/p\u003e\u003cp\u003eThis study also found significant differences in perceived risk based on age and education levels, with older and less formally educated respondents more likely to identify agricultural fields as primary conflict zones. This suggests that personal experience with elephant incursions and generational knowledge contributes to spatial awareness of risk. Such localized knowledge is essential for participatory mapping and spatial prioritization of mitigation efforts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, elephants\u0026rsquo; attraction to water sources near farms further illustrates the interplay between landscape configuration and HEC risk. Shared use of water points increases encounter probability and necessitates coordinated water management strategies, possibly including alternative water provisioning within the park or along designated elephant corridors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The presence of crop damage on the village periphery, albeit less frequent, indicates occasional deep incursions, especially in years of severe drought or heightened elephant movement, reinforcing the need for village-level surveillance and response systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Magnitude and trends in crop damage\u003c/h2\u003e\u003cp\u003eOver the five-year period assessed (2019\u0026ndash;2023), a clear escalation in crop damage was recorded, particularly in Kavambugu where damage surged from 45 acres in 2020 to 527 acres in 2023. This dramatic increase aligns with findings from [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who observed that elephant populations rebounding from anti-poaching efforts increasingly expand into agricultural lands when corridor connectivity is limited or degraded. The rising damage levels are also indicative of elephants habituating farms as reliable food sources, a behavioral shift that exacerbates long-term conflict [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe higher average damage reported in Mkonga Ijinyu compared to Kavambugu may reflect spatial differences in land cover, proximity to preferred elephant paths, or the density of farms near the boundary hence emphasizing the need for localized conflict assessment, rather than blanket policy interventions.\u003c/p\u003e\u003cp\u003eThe report that 77.2% of households lost between 1\u0026ndash;3 acres per incident is significant when compared with data on local harvest yields. For many smallholders, the loss of even a single acre represents a substantial portion of their annual food supply and income. These statistics reinforce calls by [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2\u003c/span\u003e] for more rigorous damage monitoring and standardized reporting mechanisms to inform policy and compensation frameworks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Socio-economic impacts of crop damage\u003c/h2\u003e\u003cp\u003eThe socio-economic consequences reported loss of income, food insecurity, and disrupted education are consistent with the broader literature on the indirect and long-term effects of HEC on rural livelihoods [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The finding that all respondents (100%) cited reduced school attendance due to HEC highlights the deep social penetration of this ecological problem. Children are often kept from school to assist in night guarding or because families cannot afford school fees after losing crops, compounding intergenerational poverty.\u003c/p\u003e\u003cp\u003eThe destruction of food and cash crops simultaneously undermines nutritional security and income diversity, especially in households reliant on seasonal harvests for sustenance and local trade. These pressures can fuel resentment toward wildlife authorities and erode public support for conservation, particularly when compensation mechanisms are absent or perceived as unjust [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, the overlap between food insecurity and educational disruption suggests that HEC not only affects immediate economic outcomes but also undermines the long-term development capacity of entire communities. Addressing these impacts requires a holistic strategy that goes beyond reactive deterrence to incorporate education support, livelihood diversification, and resilience-building interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Effectiveness of mitigation strategies\u003c/h2\u003e\u003cp\u003eThe predominance of night guarding (53.7%) as the primary mitigation strategy illustrates both community commitment and the absence of accessible alternatives. However, night guarding is labor-intensive, exposes individuals to physical danger, and has diminishing effectiveness against habituated elephants [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The use of combined deterrents such as noise (air horn, drums etc), fire, and chili fences shows some community-level innovation, yet these methods often suffer from inconsistent implementation and limited technical support. Notably, only 6.2% of respondents reported using beehive fences, despite their proven efficacy in East African landscapes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Low uptake may reflect high initial costs, limited training, or skepticism regarding effectiveness. This calls for targeted capacity-building programs and subsidies to promote adoption. Integrating modern tools (e.g., motion-sensor alarms, mobile-based alert systems) with traditional methods could improve efficiency, especially when coupled with community-based monitoring teams. These findings reflect broader concerns raised in the literature regarding the sustainability and scalability of current HEC mitigation strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moving forward, conflict mitigation must be embedded within integrated conservation frameworks that combine ecological science, local knowledge, and equitable benefit-sharing mechanisms [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study reveals that elephant-induced crop damage in villages adjacent to MKONAPA is spatially clustered, temporally predictable, and socio-economically devastating. While the problem is escalating, current mitigation measures remain inadequate and unsustainable. To sustainably devise effective mitigation method for HEC mitigation in the MKONAPA we recommend including stakeholders from both Tsavo and Mkomazi and harmonization of policies in both sides. These findings point to the urgent need for evidence-based HEC management strategies that integrate landscape-level planning, provision of community conservation education on HEC mitigation methods, and innovative deterrents, aligned with Tanzania\u0026rsquo;s national biodiversity goals and the Kunming-Montreal Global Biodiversity Framework (CBD, 2022).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study is available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKMH, ZMS, RAS, SHM and TK conceptualized and designed the study. ZMS collected and analyzed the data. KMH re-analyzed data and wrote the article with the support of RAS, SHM and TK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the village leaders of all the two villages in the Same District where this study was conducted for their cooperation during our data collection. The authors also send their sincere thanks to the College of African Wildlife Management (CAWM) through Research, Publication and Consultancy Unit for the logistic support that allowed the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not receive funding from a specific funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to data collection, research permit was obtained from the Research Committee from Research, Publication and consultancy Unit at the College of African Wildlife Management. The data collection procedures for respondent interviews adhered to the ethical standards of the College of African Wildlife Management and followed the principles outlined in the World Medical Association Declaration of Helsinki (WMA, 2013). Informed consent was obtained from all participants prior to their involvement in the survey. At the start of each interview, respondents were informed of their right to seek clarification at any point during the process. To maintain anonymity, participants\u0026rsquo; names were not recorded, and each questionnaire was assigned a unique identification number. The study did not involve any human health-related issues. Additionally, verbal permission to conduct research in the selected study areas was obtained from the District Executive Director of Same District, the District Game Officer, and the Village Executive Officers of Kavambughu and Mkonga Ijinyu villages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespondents were informed about the objectives of the study and the eventual publications of the information collected and were assured that their identities would remain undisclosed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKansky R, Kidd M, Knight AT. Meta-Analysis of Attitudes toward Damage-Causing Mammalian Wildlife. 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Int J Biodivers Conserv. 2020;12(3):227\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHariohay, K.M., M.A. Cletus, L.E. Happygod, H. Louis, K.J. Ramadhan, and E. and R\u0026oslash;skaft,\u003cem\u003eCan conservation-based incentives promote willingness of local communities to coexist with wildlife? A case of Burunge Wildlife Management Area, Northern Tanzania.\u003c/em\u003e Human Dimensions of Wildlife, 2024. 30(2): pp. 149\u0026ndash;162. https://doi.org/10.1080/10871209.2024.2333550. \u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human-elephant conflict, Crop raiding, Spatio-temporal analysis, Mkomazi National Park","lastPublishedDoi":"10.21203/rs.3.rs-7093604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7093604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman-elephant conflict (HEC) poses a major threat to biodiversity conservation. This study examined the spatio-temporal patterns and socio-economic impacts of elephant-induced crop damage in Mkonga Ijinyu and Kavambughu villages adjacent to MKONAPA, Tanzania. Data were collected through household surveys (n\u0026thinsp;=\u0026thinsp;162), key informant interviews, and secondary data records from 2019 to 2023. Results revealed that crop damage was spatially concentrated within 1 km of park boundaries and temporally peaked during harvest seasons from April to July and in October. Maize (88.3%), beans (76.5%), and sunflower (64.2%) were the most frequently damaged crops. Crop raids occurred predominantly at night (98.8%), with elephants destroying up to one acre in a single event. Kavambughu experienced the highest annual loss (527 acres in 2023), while Mkonga Ijinyu reported higher average damage per incident. This finding indicates that crop raiding follows predictable patterns tied to crop maturation, proximity to wildlife habitat, and seasonal forage scarcity inside the park. These behavioral adaptations by elephants amplify conflict intensity and highlight the urgency of targeted interventions. Socio-economically, over half of affected households reported food insecurity and income loss, while all respondents noted reduced school attendance among children demonstrating the broader developmental consequences of HEC. The escalation of elephant-induced crop damage jeopardizes both conservation outcomes and local well-being. This study recommends integrated mitigation strategies including buffer zone reinforcement, adoption of innovative deterrents (e.g., beehive fences and early warning systems), and stronger community engagement. Policy frameworks should prioritize compensation and ecosystem-based planning to foster long-term human-elephant coexistence.\u003c/p\u003e","manuscriptTitle":"Elephant crop raiding in northern Tanzania: Spatio-temporal trends and damage assessment in villages adjacent to Mkomazi National Park","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:20:28","doi":"10.21203/rs.3.rs-7093604/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ebc6a82-d873-4add-a733-8048ccecea7d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:25:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:20:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7093604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7093604","identity":"rs-7093604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}