The impact of predation in communal sheep flocks in the central Free State Province, South Africa

33 NATURAL SCIENCES Indago, Vol. 39, 12 October 2025, pp. 33–44 urn:lsid:zoobank.org:pub:7FD61B68-7555-48E2-9308-FE713FAB9CA2 Received: 31 May 2023 / Revised: 1 Dec. 2023 / Accepted: 24 Jul. 2024 DOI: 10.5281/zenodo.17255374 ISSN (print) 0067-9208 The impact of predation in communal sheep flocks in the central Free State Province, South Africa Andries J. Strauss1,2*, Jan Willem Swanepoel2 & Jasper J. E. Cloete3 1Free State Department of Agriculture and Rural Development, Private Bag X01, Glen, 9360 South Africa 2Department of Sustainable Food Systems and Development, Faculty of Natural and Agricultural Sciences, University of the Free State, P.O. Box 339, Bloemfontein, 9300 South Africa 3Western Cape Department of Agriculture: Elsenburg Agricultural Training Institute, Private Bag X1, Elsenburg, 7607 South Africa ORCID: Strauss, A.J.: https://orcid.org/0000-0001-5248-7285 ORCID: Swanepoel, J.W.: https://orcid.org/0000-0002-0812-2657 ORCID: Cloete, J.J.E: https://orcid.org/0009-0007-0375-4149 *Corresponding author: [email protected] ABSTRACT The communal wool farmers in the Thaba Nchu and Botshabelo area in the Free State Province struggle to improve their livelihoods. Various limitations, such as diseases and parasites, livestock theft, insufficient capital, high feed costs, challenges in marketing, labour constraints, elevated rent expenses, and a lack of professional knowledge, hinder their ability to make significant economic contributions. This study describes the composition of facts and impacts related to predation in communal sheep flocks in the central Free State Province, following a cross-sectional descriptive study using quantitative methods. During interviews, researchers utilised a structured questionnaire to gather quantitative data on small-stock losses over a 12-month period. Data were collected from 351 (28 %) of the 1255 communal sheep farmers in the Thaba Nchu and Botshabelo districts. The mean monetary worth of a typically communal farmer sheep flock (mean = 27.36 sheep/farmer) was ZAR 30,861.96/farmer, with an attested reduction of 49.2 % of the flock (ZAR 16,103.99/farmer) reported by the farmers. The losses were due to various factors, such as diseases and parasites (37.4 %), weather conditions (drought, flood, storms) (22.1 %), theft (23.2 %), predation (12.1 %), disputes over pastures and water (4.6 %), and other losses (metabolic disorders and accidents) (0.6 %). Loss due to predation in communal sheep flocks was 5.9 % of the total flock size. Predation results in the loss of 1.63 per 27.36 sheep in the communal flock. Most farmers did not experience any loss due to predation; in most cases, the loss was not more than five sheep per farmer. There was a statistically significant association between the number of sheep older than 12 months and loss due to predators, χ2(2) = 7.83, p = 0.02. Participants with more than five sheep older than 12 months were likelier to suffer from predators. Most predation loss was due to unpredictable killings by vagrant domestic dogs. Shepherding played the most vital role in reducing damages due to predators. KEYWORDS: Depredation, diseases, farming, livestock, predators, theft, vagrant domestic dogs, weather, South Africa INTRODUCTION Predation and its consequential impacts bear national ramifications on food security, employment rates and socio-economic deterioration, particularly within small -stock farming in South Africa (Avenant & Berg man 2021). In 2019, the quantifiable expenses incurred due to predation-related losses in small and large livestock reached ZAR 2,710 million and ZAR 511 million, respectively (Van Niekerk et al. 2021). The data and their implications for biodiversity and policy formulation predominantly originate from the commercial sector, while the comprehension of predation’s influence remains inadequate in communal farming regions of South Africa (Turpie & Akinyemi 2018). A first intensive 5-year study quantified small-stock losses (ZAR 1,218,283) in the sheep flock (854) of the Glen Agricultural College in the Free State Province, South Africa (physically identified losses vs farmer questionnaires). Predation exerted a more significant impact (72 %) on the overall flock size compared to the combined effects of diseases (2 %), metabolic disorders and accidents (20 %) and livestock theft (6 %) (Strauss et al. 2021). The primary predators accountable for most economic losses in agricultural industries are Black-backed jackals (Lupulella mesomelas) and Caracal (Caracal caracal). Additionally, vagrant domestic dogs (Canis familiaris) can substantially harm small livestock, par ticularly near human settlements (Strauss et al. 2021; Van Niekerk et al. 2021). According to Strauss (2009), about 70 km west of the Thaba Nchu – Bo tshabelo area, vagrant domestic dogs, for example, were responsible for the second most signi fi cant num ber of losses, 20 % of kills (actual range of 13–26 %). In the same study, Black-backed jackals were res ponsible for 70.2 %, and Caracal for 10 % of losses. Limited and reliable predation management infor mation is available from other provinces and com munal areas in South Africa (Thorn et al. 2013; Constant 34 Strauss et al. — Impact of predation in communal sheep flocks 2014). A lack of recordkeeping, especially of small stock production and marketing by the communal farmers in the Thaba Nchu and Botshabelo area in the Free State, hinders them from determining their main constraints and makes them vulnerable to fraud. Poor recordkeeping makes it difficult for farmers to track their inventory effectively. This can result in theft or unauthorised livestock sales, as there may be no clear documentation of the number of sheep owned by the farmers and their current status. Various factors, such as diseases and parasites, livestock theft, insufficient capital, high feed costs, challenges in marketing, labour constraints, elevated rent expenses, and a lack of professional knowledge, hamper farmers’ ability to make significant economic contributions (Nyam et al. 2022). Animals in poor shape may also be more vulnerable to predation. Low rainfall, through its effect on vegetation, may adversely affect animal health (Levine et al. 2008). Diseases, parasites and hostile climatic conditions that contribute to drought and overgrazing are frequent in communal areas in the Free State Province (Van der Westhuizen et al. 2020). Sick and vulnerable livestock are at higher risk of being killed, e.g. by Black-backed jackals when predators’ other food sources diminish during droughts or overgrazing (Scheiss-Meier et al. 2007; Bahta et al. 2016; Strauss et al. 2021). Predation increases when proper herding is lacking, a usual situation in communal farming areas (Lutchminarayan 2014; Hawkins et al. 2023). The impact of predation on the small stock in communal areas in South Africa can devastate both indi vidual farmers and entire communities (Turpie & Aki nyemi 2018). Animal, especially small stock, hus bandry is a significant source of income and liveli hood for many rural farmers and losing only a few sheep can substantially impact their financial sta bility. Predation misfortune adversities is often exa cerbated by many small-stock farmers having limited resources and infrastructure to protect their animals from predators (Lutchminarayan 2014). This study describes the composition of facts and im pacts related to predation in communal sheep flocks in the central Free State Province, following a cross-sectional descriptive study using quantitative methods. MATERIALS AND METHODS Study area The study was conducted in the Thaba Nchu–Botshabelo area of the Mangaung district in the Free State Figure 1: Map depicting surveyed communities in the Thaba Nchu – Botshabelo area in the Mangaung district in the central and south-eastern parts of the Free State Province, South Africa. (DARD, GIS section, 2023) 35 Indago Vol. 39 (2023–2025) Province, South Africa (Fig. 1). This region has flat, rolling grasslands, crop fields, isolated sandstone ko pjes and mountains. The province is a crop and live stock production area divided into five districts: Man gaung, Xhariep, Lejweleputswa, Fezile Dabi and Thabo Mofutsanyana. However, focus groups for per sonal communication and training were canvassed in the Thaba Nchu and Botshabelo area in the Man gaung district, where most communal farmers are situated. The Thaba Nchu and Botshabelo area has 40 com - mu nities (59,957 ha grazing area). Among these, 21 (53%)—encompassing 30,182 ha of grazing land featu ring various vegetation types, specifically sweet grass veld, sour grass veld, and mixed sour grass veld (Van der Westhuizen et al. 2018)—were chosen to ensure a balanced coverage across the study area. The climate in the area is characterised by cold winters with an average daily minimum of -0.6 °C with frost, and hot summers with an average daily maximum of 33.6 °C (Fig. 2) (ARC-NRE 2023). The long-term annual rainfall (6 June 2012 – 29 January 2022) for the Botshabelo and Thaba Nchu area is 546 mm (Fig. 3). Most of the rainfall (>70 %) is received in the summer from November to March (Fouché et al. 1985). The Free State Department of Agriculture and Rural Development (DAFF 2018) recommends a long-term grazing capacity of 6 ha per Large Stock Unit (LSU) for the Botshabelo and Thaba Nchu area (Meissner et al. 1983). Sampling procedure The participants were selected via a non-random, pur posive, convenience sampling method to reach the communal wool farmers in the Mangaung dist rict. Ap proximately 1255 small-stock communal farmers were in the Mangaung district (Mocwiri 2022). A sample size calculator (http://www.raosoft.com/ samplesize.html) was used to determine the sample size to generalise the population. For this study, 295 or more observations have a confidence level of 95 %, with the actual value within ± 5 % of the observation value. Data collection procedure Ethical clearance was obtained from the University of the Free State Research Ethics Committee for this project (UFS-HSD2021/0422/21). A structured questionnaire was used in one-on-one interviews to obtain quantitative data on small-stock losses. It took approximately 40 min to complete the 73 ques tions in the questionnaire. The designed question naire captures household demographics, tempo rary and permanent farm workers, farming experience, the primary source of farming knowledge ob tained, small-stock losses, infrastructure, involvement of extension services and input costs. While the survey was in English, all interviews took place in the participants’ native languages following the interpretation of the English version. For the interviews, 16 final-year agricultural students at the University of the Free State received training in the ques tionnaire. Most of these enumerators’ home language was Sesotho, but Setswana, isiZulu and Figure 2: Maximum (Tx) and minimum temperatures (Tn) and the long-term average temperatures (9.7 years) from Woonhuis weather station (29.15276 °S 26.57214 °E; alt. 1269 m a.s.l.) 25 km west of the Thaba Nchu – Botshabelo area. (ARC-NRE 2023) 36 Strauss et al. — Impact of predation in communal sheep flocks isiXhosa-speaking enumerators were also selected. This enabled the participants, mainly Sesotho-speaking, to talk openly with little restriction. Throughout the data collection phase, supervisors systematically observed enumerators to maintain uniformity, detect outliers, and ensure the precision of the gathered data. The researcher disseminated the questionnaire to respondents and assured them that their data would be confidential. Extension officers recruited participants by making use of an inclusion criteria list. Small stock com prises wool sheep, dual-purpose sheep, mutton sheep and goats. Although there were about 1255 small-stock farmers, only those farming wool-type sheep were included. Data collection was from November 2021 until February 2022, when most of the ewes would have lambed and the lambs weaned (Strauss et al. 2021). The data collected (e.g., farmer demographics, livestock numbers, pro duc tion, reproduction, losses) included one year’s data (2021/2022). Annual di rect allocable variable costs (DAVC) (veterinary services and me di cine, transport, fodder, fertiliser, farm mecha nisation, ma chi nery/equipment hire, repair and main tenance, and operational costs: banking, cell phone, marketing and branding) were also col lected and to further the study, additional financial cal culations were neces sary. The following direct losses over 12 months were dependent variables in the questionnaire that affected livestock owners and agricultural production in general: • Disease and parasite: This referred to losses caused by livestock diseases and infestations of parasites that could lead to illness and death of the animals. Such losses may be due to inadequate veterinary care and/or insufficient preventative measures. • Drought: This referred to losses triggered by a prolonged period of dry weather, which could lead to a shortage of water and feed for livestock and, in extreme cases, death of the animals. • Floods: This referred to losses provoked by heavy rain fall or river overflow, which could result in the drowning of livestock, damage to infrastructure and buildings, and loss of crops. Floods can also lead to soil erosion, further affecting agricultural pro duc tivity. • Thunder/Lightning: This referred to losses caused by lightning strikes, which could lead to the death or injury of livestock and damage to buildings and infrastructure. Thunder can also cause stress and an xiety in livestock, affecting their productivity. • The dependent variables ‘drought’, ‘floods’ and ‘thun der /lightning’ were grouped as losses due to weather conditions. • Disputes over pasture and water: This variable referred to losses caused by conflicts over access to grazing lands and water sources, which might have re sulted in overgrazing, soil degradation and de creased produc tivity. In most farming com munities, ex ten sive grazing areas are unfenced, and sometimes, the sheep flocks of different commu nities or even small stock from Lesotho get mixed up. Disputes then arise due to differences in ownership (lack of ani mal identification marks and record keeping) or traditional use rights. The winning party of the dis pute gains the sheep and the losing party registers a loss. • Livestock killed by predators/dogs: This variable reflected losses caused by attacks from predators like the Black-backed jackal, Caracal and vagrant Figure 3: Rainfall data for 2020, 2021 and long-term average rainfall (9.7 years) from Woonhuis (29.15276 °S 26.57214 °E; alt. 1269 m a.s.l.) weather station 25 km west of the Thaba Nchu – Botshabelo area. (ARC-NRE 2023) 37 Indago Vol. 39 (2023–2025) do mestic dogs (de Waal 2009, 2021; Van Niekerk 2010), which could lead to injury or death of livestock. • Theft: This referred to losses caused by the theft of livestock, which resulted in economic damages for the communal farmers. • Other: This dependent variable referred to losses cau sed by other factors not explicitly mentioned above, such as accidents and metabolic disorders (e.g., bloat and acidosis). These losses may be less familiar but significantly impact agricultural productivity and livelihoods. The following independent (explanatory) variables ex plain or predict changes in a dependent variable, namely predation: • The period the communal farmer was practising wool production; • Monthly income; • Farm infrastructure (absence or condition of fen ces and kraals to safeguard the sheep); • Sheep demographics (flock structure); • The number of permanent and temporary staff to safeguard the sheep. Data management and analysis According to Van Niekerk (2002), using questionnaire survey methods enables researchers to gauge an indi vidual’s knowledge, the nature of the infor ma tion they hold, their values and beliefs, and their attitudes toward the questionnaire topic. When admi nis tering a questionnaire survey, participants respond more favourably, and the results are superior to telephonic interviews or mail surveys (Van Niekerk 2002). The questionnaire in this study was created by using the EvaSys software. The SPSS (ver. 29) program processed and analysed the data. Scanned comple ted questionnaires identified outliers and potential errors. The mean values for missing observations were added to calculate the sum in each category. Average prices of livestock auctioneers in Bloemfontein, ± 60 km from the study area, were used to calculate the value of communal farmers’ sheep flocks. Chisquare ana lysis was done for descriptive statistics and factors affecting sheep losses due to predation. The results of the analysis is presented below in the form of tables, graphs and text. RESULTS AND DISCUSSION Structure of the sheep flocks in the study area Data from 9603 sheep were obtained through interviews with 351 farmers. The minimum number of sheep owned by an individual was 1, while the maxi mum was 200 (Table 1). The average number of sheep owned by the population was 27.36 ± 32.11. This number also included lambs (Table 2). Overall, the findings provide insight into the sheep ownership patterns. Financial value of the communal farmers’ sheep flocks The financial value of the sheep was determined by the categorised flock structure multiplied by the onthe-hoof prices in February 2023 (Myburg 2023) and the average live weights (birth-, weanedand 12-month weights) for sheep in extensive grazing con di tions (Strauss 2009). The total number of sheep in each category was calculated with the average value per sheep to estimate the total collection of all sheep (Table 2). The total value of 28 % of the commu nal farmers’ sheep flocks was estimated at ZAR 10,832,547.45 (ZAR 30,861.96/farmer), with the highest contribu tion coming from ewes over one year of age (ZAR 5,769,553.50) (Table 2). Therefore, the value for the 1255 communal wool farmers in the BoTable 1. Descriptive statistics of the communal sheep flocks. Total number of sheep NValid 351 Missing 0 Mean 27.36 Median 17.00 Mode 4 Std. deviation 32.11 Range 199 Minimum 1 Maximum 200 Sum 9603 38 Strauss et al. — Impact of predation in communal sheep flocks tsha belo and Thaba Nchu area can be estimated at (ZAR 30,861.96 × 1255 = ZAR 38,731,759.80). The cost of the different farming inputs per year was allocated to all livestock activities. The total LSU for sheep were obtained by multiplying the total number of sheep with the LSU in each category: 0–4 months (0.08 LSU), 5–12 months (0.15 LSU), ewes >1 year (0.17 LSU) and rams >1 year (0.25 LSU) (Meissner et al. 1983). Then, the LSUs were calculated for all livestock to determine the contribution in each cate gory (Table 3). The sheep LSU contribution was 34.4 %, which was used to calculate the annual costs/ sheep (Table 4). Financial implications of losses and mortalities in the communal farmers’ sheep flocks The annual direct allocable variable cost for communal sheep flocks can be analysed using various compo nents, each contributing a specific percentage to the total amount (Table 4): Labour refers to expenses associated with hiring and compensating personnel (temporary or permanent farm workers/managers) involved in the management and care of communal sheep flocks. This includes feeding, shearing, general flock maintenance and, most importantly, herding. The labour cost of ZAR 25.33/sheep (15.73 %) was among the highest contributors to the cost per sheep (Table 4) and varied between seasons and communities. The labour cost in our survey was three-fold higher than in a study on the profitability of Merino sheep (casual labour, 5 %) (Geyer & Venter 2015). Veterinary medicine and services are essential for maintaining the health and well-being of the communal sheep flocks. The component was the highest allocable cost per sheep (ZAR 28.69/sheep, or 17.82 %; Table 4) and included veterinary con sul tations, vaccinations, medications, dosing and any specialised services required. The cost in this category was the same (17.6 %) in the study by Geyer and Venter (2015) and only slightly higher compared to a similar sheep flock at FSDARD (Glen) (11.9 %) with no extra veterinary cost because state veterinarians took care of the state-owned sheep (Strauss et al. 2021). Transport (incl. fuel) encompasses expenses as so ciated with moving the sheep and transporting wool and related materials from one location to another. The amount of ZAR 26.40/sheep (16.39 %) in cluded fuel costs (bakkie/tractor), vehicle/tractor main tenance, and other transport-related expenses, and was the third highest allocable cost per sheep (Table 4). Fodder cost represents the expenses incurred to feed the communal sheep flocks. This includes purchasing or producing hay like lucerne, planted pastures, and supplementary feeding: maize, licks, pellet con cen trates and mineral salt. Only a few pastures (pri marily oats) and maize are self-produced to assist with fodder flow. The amount of ZAR 26.98 (16.75 %) was the second-highest allocable cost per sheep (Table 4). Geyer and Venter (2015) indicated that feeding cost was 58 % of the total DAVC, i.e. significantly higher than the feeding cost (16.75 %) reported in our survey. Communal farmers depend primarily on the government assistance, especially fodder, which explains the marked difference. Machinery/equipment hire, repair and maintenance incurred expenses of ZAR 5.26/sheep (3.27 %) associated with the rental, repair and maintenance of machinery (e.g. sheep shearing) and equipment (e.g. wool press, dosing gun) used in communal sheep farming (Table 4). The other allocable costs like shearing (ZAR 17.07/ sheep, 10.62 %), operational costs (ZAR 7.50/sheep, 4.66 %), farm mechanisation (ZAR 15.05/sheep, 9.34 %), fertiliser (ZAR 4.54/sheep, 2.82 %), and mar keting and branding (ZAR 3.94/sheep, 2.62 %) also contributed to the total of ZAR 163.40/sheep (Table 4). The financial loss in sheep flocks was substantial, amounting to ZAR5,652,500.51 (ZAR16,103.99/farmer) (Table 5). As most farmers could unfor tu na tely not ascribe losses in specific age classes to certain causes, the average sheep value of ZAR1036.56 and ZAR160.75 cost/sheep (Tables 2, 4) was used to calTable 2. The financial value (ZAR) of the communal sheep flocks per age category. The assumption is that each category has a uni form distribution of values. Age category Mean, kg Mean, R/kg ZAR/sheep Total number of sheep (mean and std. dev.) Total value, ZAR 0–4 months 4.9 38.50 188.65 (5.6 ± 7.7) 1947 367,301.55 5–12 months 24.6 38.50 947.10 (6.0 ± 8.1) 2094 1,983,227.40 Ewes >1 year 53.0 25.50 1351.50 (12.2 ± 12.6) 4269 5,769,553.50 Rams >1 year 79.0 21.00 1659.00 (4.7 ± 6.2) 1635 2,712,465.00 av. 1036.56 10,832,547.45 39 Indago Vol. 39 (2023–2025) culate the total financial losses in Table 5. The fi nancial implications in the ca te gory diseases and pa rasites (ZAR2,114,449.46) were significantly higher in all categories of losses like theft (ZAR1,308,659.83), weather conditions (ZAR1,252,386.26) and predation (ZAR683,664.01). Total losses in communal farmers’ sheep flocks The number of losses due to various factors such as diseases and parasites, extreme weather, disputes over pasture and water, predation, theft, metabolic disorders, and accidents totalled 4721, equivalent to 49.2 % of the total sheep (Table 5). The highest percentage of losses was attributed to diseases and parasites (37.4 %), followed by weather conditions (droughts, floods, storms) (22.1 %), theft (23.2 %) and predation (12.1 %) (Fig. 4). During the study period (2021/2022), above-normal rainfall was recorded for both 2020 (763 mm) and 2021 (738 mm) (Fig. 3). The abnormal differences between the maximum and minimum temperatures, e.g., in July 2021 (Tx = 23.5, Tn = -5.1), together with belowaverage rainfall (March – July 2021) and the aboveaverage rainfall (August 2021 – Jan 2022) (Figs 2, 3), can explain the high number of losses to diseases and parasites and to weather conditions (Fig. 4). Losses due to disputes over pastures and water (4.6 %) were relatively low. However, the financial value of the loss (ZAR 261,013.58; Table 5) was still substantial. It could affect the well-being of communal farmers tremendously. A lack of recordkeeping and animal identification also contributed to losses due to disputes over pastures and water when livestock gets mixed up between communities and with livestock from Lesotho. In comparison with other studies by Strauss et al. (2021) in the central Free State, other losses (metabolic disorders and accidents) were relatively low (0.6 %; Fig. 4). The majority of communal farmers (58 %) did not engage in recordkeeping practices, while 29 % maintained partial records, and only 13 % maintained detailed records. Consequently, the reported losses might exhibit a potential bias; ne vertheless, poor maintenance of documentation significantly influenced communal farmers’ manage ment practices and long-term sustainability. Strauss et al. (2021) indicated that lamb losses due to predation were up to 81.6 % of lambs born, and post-weaned losses were 18.6 % of the total flock size over nine years. According to Viljoen (2015), lamb losses due to predation on the National Wool Growers Association monitor farms were also a staggering 46 %, with 89 % occurring before weaning age. Although losses due to predation (5.9 %, or Table 3. Total Large Stock Units (LSUs) for communal farmers’ livestock in Thaba Nchu and Botshabelo area. Calculation of different categories according to Meissner et al. (1983). Description Livestock number LSUs % Sheep 9603 1604 34.4 Cattle 2962 2962 63.5 Goats 610 98 2.1 4664 100 Table 4. Annual direct allocable variable costs (ZAR) for communal sheep flocks (2021–2022). Description Annual cost (9603 sheep) Cost per sheep % of total cost Labour 243,198.95 25.33 15.73 Veterinary medicine and services 275,497.35 28.69 17.82 Transport (including fuel) 253,503.25 26.40 16.39 Fodder* 259,062.39 26.98 16.75 Fertiliser 43,559.03 4.54 2.82 Farm mechanisation 144,485.26 15.05 9.34 Machinery/equipment hire, repair & maintenance 50,513.39 5.26 3.27 Operational: banking, cell phone 72,086.29 7.50 4.66 Marketing and branding 37,831.40 3.94 2.62 Shearing ** 163,959.00 17.07 10.62 Total 1,543,696.31 160.75 100 * Planted pastures, supplementary feeding, e.g. lucern, maize, licks, pellet concentrates and mineral salt. ** This included shearer, woolhandler, and classer for only rams, ewes and lambs 5–12 months. 40 Strauss et al. — Impact of predation in communal sheep flocks ZAR 683,664.01; Table 5) in sheep flocks were still enormous, they were markedly lower than those in commercial sheep flocks (up to 13 %, amounting to R2.34 billion (Turpie & Akinyemi 2018)) but comparable to losses of 2.98–7.35% due to predation on farms in Central Karoo (Conradie & Nattrass 2017). The losses caused by predation were 1.63 per 27.36 sheep (Table 6), the average flock size per farmer, representing a loss of 5.9 % (Table 5). While this remains a considerable loss, most farmers (61.3 %) reported no losses due to predation. Most losses were no more than five sheep per farmer. The Black-backed jackal was responsible for only a few losses, and no losses to Caracal were reported. However, vagrant domestic dogs were responsible for most of the damages, contradicting findings from other studies in South Africa, where the Blackbacked jackal was responsible for over 70 % of the losses (Strauss et al. 2021). Most sheep were lost due to diseases and parasites, with 69 % of participants indicating some loss and most (39 %) indicating a loss of 1–5 sheep during the preceding 12 months. Loss due to predators was the fourth contributing factor (following weather and theft), with 36 % of participants experiencing loss due to predators and most (31 %) indicating a loss of 1–5 sheep during the preceding 12 months. Although stock theft could have substantial financial implications, most farmers did not experience it (Fig. 4). Unfortunately, if the stock theft syndicates operate in a specific area, they can eliminate the entire communal sheep flock. This happened in the Springfontein community, where three farmers lost all their sheep to organised crime groups. In 2022, 38,000 livestock theft cases were reported in the Free State Province, of which 21.6 % were from the neighbouring towns next to the Lesotho border (Louw 2023). Springfontein community in the Thaba Nchu district is a bare 30 km from the Lesotho border. However, criminal syndicates do not continuously operate from Lesotho. Many stock theft losses occur more profoundly within the South African borders. Sheep are the working capital of the communal farmers; therefore, the capital needs to be replaced when stock theft occurs (NSTPF 2015). Many communal sheep farmers indicated that they were selling their livestock to ensure some income instead of losing everything. Although other losses were more remarkable, selling the only source of income would cause serious financial problems for the farmer, the entire community, and unemployment in the Free State Province. Table 5. The total financial implication (in ZAR) of losses and mortalities in the surveyed communal sheep flock (9603 sheep). Losses Number of losses (% of total flock) 1036.56 av./sheep + 160.75 cost/ sheep Total Diseases & parasites 1766 (18.4 %) 1197.31 2,114,449.46 Weather conditions (drought, flood, storms) 1046 (10.9 %) 1197.31 1,252,386.26 Dispute over pasture and water 218 (2.3 %) 1197.31 261,013.58 Predation 571 (5.9 %) 1197.31 683,664.01 Theft 1093 (11.4 %) 1197.31 1,308,659.83 Other losses: Metabolic disorder and accidents 27 (0.3 %) 1197.31 32,327.37 Total losses 4721 (49.2 %) 5,652,500.51 Figure 4: Attributes of sheep losses reported by 351 communal farmers in the Central Free State expressed as a percentage of the total losses. 41 Indago Vol. 39 (2023–2025) Causes of predation losses in communal farmers’ sheep flocks Chi-square analysis showed a statistically significant association between the number of sheep older than 12 months and loss due to predators, χ2(2) = 7.83, p = 0.02. Participants with more than five sheep older than 12 months were likelier to experience loss due to predators (Table 7). Strauss et al. (2021) indicated that kraaling was one of the successful non-lethal methods to mitigate predation. In the present study, the condition of the farm fences and kraals had no significant (p>0.05) influence on reducing losses due to predation. Most losses were due to vagrant domestic dogs. Almost every household had dogs and could not predict when those dogs or stray dogs would cause losses irrele vant to the conditions of the fences and kraals, their farming experience and monthly income. Safeguarding the sheep against predators by temporary or permanent workers evidently influenced the scale of this dam age. The farmers indicated that where losses did occur, 57 % of them were associated with no farm workers. When labourers were appointed—1 or 2 workers, or more than two workers—the losses due to predation decreased markedly, by 27 % and 17 %, respectively (Table 7). The lessen predation losses to mesopredators could be attributed to improved shepherding. A recent study indicated that predation losses were five-fold lower when shepherding was implemented compared to areas where shepherding was not part of the farmer’s management practices (Hawkins et al. 2023). Conradie and Piesse (2015) Table 6. Descriptive statistics for factors affecting the loss of sheep in communal flocks. Diseases and parasites Weather conditions Disputes Predation Theft Other losses N Valid 351 351 351 351 351 351 Missing 0 0 0 0 0 0 Mean 5.03 2.98 0.62 1.63 3.11 0.08 Median 3.00 0.00 0.00 0.00 0.00 0.00 Mode 3 0 0 0 0 0 Std. deviation 5.609 4.896 2.291 2.767 6.121 0.475 Range 21 21 21 21 21 3 Minimum 0 0 0 0 0 0 Maximum 21 21 21 21 21 3 Sum 1766 1046 218 571 1093 27 Key: 1 = 0, 2 = 1–5, 3 = 6–10, 4 = 11–20, 5 = 21+ Table 7. Factors affecting the loss of sheep due to predation. Losses due to predators p-valueNo Yes n % n % Period practising wool production Five years or less More than five years 107 94 53 % 46 % 55 71 44 % 56 % 0.09 Monthly income ZAR3330 or less More than ZAR3330 146 52 74 % 26 % 92 30 75 % 25 % 0.74 Number of sheep 12 months or younger None 1–5 More than 5 29 150 24 14 % 74 % 11 % 16 86 25 12 % 68 % 20 % 0.15 Number of sheep older than 12 months None 1–5 More than 5 16 139 48 8 % 68 % 24 % 5 75 47 4 % 59 % 37 % 0.02 Condition of farm fences/ kraals Working condition Not in working condition/absent 124 79 61 % 39 % 86 41 68 % 32 % 0.22 Number of workers (temporary and permanent) No workers 1 or 2 workers More than 2 workers 100 78 25 49 % 38 % 12 % 72 34 21 57 % 27 % 17 % 0.08