Refining livestock mortality indicators: a systematic review

Leveraging browse and grazing forage estimates to optimize index-based livestock insurance

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

Calculation of livestock biomass and value by province in Indonesia: Key information to support policymaking

Accurate estimations of the biomass and value of livestock in Indonesia are of great use in supporting investment decisions by the public and private sector and as a basis for estimating the losses due to animal disease. Biomass and the partial direct use value for key livestock species (cattle, buffalo, sheep, goats, pigs, chickens) for all provinces of Indonesia were derived from secondary data using a novel spreadsheet-based model. Using beef cattle as an example, we also explored the use of a herd dynamics model to validate base data on populations and productivity used to generate biomass values, and these were found to be generally robust. Total partial direct use value of livestock is estimated to be almost USD54 billion in 2021, comprising almost USD33 billion of population value and almost USD21 billion of production value. Beef cattle account for 44% of total value and chicken (broiler, layer and native chickens) account for a further 36% of the total. Breaking the data down by province reveals the regional importance of some livestock types that are of relatively minor importance nationally (pigs in East Nusa Tenggara and sheep in West Java). It also reveals the importance of livestock in the poorest provinces of Indonesia, where livestock acts as a store of wealth and serves socio-cultural purposes.

Perinatal mortality in German dairy cattle: Unveiling the importance of cow-level risk factors and their interactions using a multifaceted modelling approach

Perinatal mortality (PM) is a common issue on dairy farms, leading to calf losses and increased farming costs. The current knowledge about PM in dairy cattle is, however, limited and previous studies lack comparability. The topic has also primarily been studied in Holstein-Friesian cows and closely related breeds, while other dairy breeds have been largely ignored. Different data collection techniques, definitions of PM, studied variables and statistical approaches further limit the comparability and interpretation of previous studies. This article aims to investigate the factors contributing to PM in two underexplored breeds, Simmental (SIM) and Brown Swiss (BS), while comparing them to German Holstein on German farms, and to employ various modelling techniques to enhance comparability to other studies, and to determine if different statistical methods yield consistent results. A total of 133,942 calving records from 131,657 cows on 721 German farms were analyzed. Amongst these, the proportion of PM (defined as stillbirth or death up to 48 hours of age) was 6.1%. Univariable and multivariable mixed-effects logistic regressions, random forest and multimodel inference via brute-force model selection approaches were used to evaluate risk factors on the individual animal level. Although the balanced random forest did not incorporate the random effect, it yielded results similar to those of the mixed-effect model. The brute-force approach surpassed the widely adopted backwards variable selection method and represented a combination of strengths: it accounted for the random effect similar to mixed-effects regression and generated a variable importance plot similar to random forest. The difficulty of calving, breed and parity of the cow were found to be the most important factors, followed by farm size and season. Additionally, four significant interactions amongst predictors were identified: breed-calving ease, breed-season, parity-season and calving ease-farm size. The combination of factors, such as secondiparous SIM breed on small farms and experiencing easy calving in summer, showed the lowest probability of PM. Conversely, primiparous GH cows on large farms with difficult calving in winter exhibited the highest probability of PM. In order to reduce PM, appropriate management of dystocia, optimal heifer management and a wider use of SIM in dairy production are possible ways forward. It is also important that future studies are conducted to identify farm-specific contributors to higher PM on large farms.

Bovine mortality: the utility of two data sources for the provision of population-level surveillance intelligence

Introduction

The use of existing data to provide surveillance intelligence is widely advocated but often presents considerable challenges. Two data sources could be used as proxies for the mortality experienced by the Scottish cattle population: deaths recorded in the mandatory register [Cattle Tracing System (CTS)] and fallen stock collections by the National Fallen Stock Company (NSFCo) with a nationwide voluntary membership.

Methods

Data for the period 2011-2016 were described and compared to establish their strengths and limitations. Similarities and differences in their temporal, seasonal and spatial patterns were examined overall, at postcode area level and for different age groups. Temporal aberration detection algorithms (TADA) were fitted.

Results

Broadly, similar patterns were observed in the two datasets; however, there were some notable differences. The observed seasonal, annual and spatial patterns match expectations, given knowledge of Scottish cattle production systems. The registry data provide more comprehensive coverage of all areas of Scotland, while collections data provide a more comprehensive measure of the mortality experienced in 0-1-month-old calves.

Discussion

Consequently, estimates of early calf mortality and their impact on the livestock sector made using CTS, or successor registers, will be under-estimates. This may apply to other registry-based systems. Fitted TADA detected points of deviations from expected norms some of which coincided in the two datasets; one with a known external event that caused increased mortality. We have demonstrated that both data sources do have the potential to be utilized to provide measures of mortality in the Scottish cattle population that could inform surveillance activities. While neither is perfect, they are complementary. Each has strengths and weaknesses, so ideally, a system where they are analyzed and interpreted in parallel would optimize the information obtained for surveillance purposes for epidemiologists, risk managers, animal health policy-makers and the wider livestock industry sector. This study provides a foundation on which to build an operational system. Further development will require improvements in the timeliness of data availability and further investment of resources.

Identifying the patterns and sizes of the first lumpy skin disease outbreak clusters in Northern Thailand with a high degree of dairy farm aggregation using spatio-temporal models

Lumpy skin disease (LSD) is one of the most important notifiable transboundary diseases affecting cattle in many parts of the world. In Thailand, LSD outbreaks in cattle farming areas have been reported in 69 out of 77 provinces, indicating a serious nationwide situation. Understanding the dynamics of spatial and temporal LSD epidemic patterns can provide important information on disease transmission and control. This study aims to identify spatial and temporal clusters in the first LSD outbreaks in dairy farming areas with a high degree of aggregation in Northern Thailand using spatio-temporal models. The data were obtained from an official LSD outbreak investigation conducted between June and August 2021 on dairy farms (n = 202). The outbreak of LSD was confirmed by employing clinical observations and laboratory analysis. The spatio-temporal models including space-time permutation (STP), Poisson, and Bernoulli were applied to the outbreak data with the settings of 10%, 25%, and 50%, respectively, for the maximum reported cluster size (MRCS). Overall, the number of most likely and secondary clusters varied depending on the model and MRCS settings. All MRCS settings in the STP model detected the most likely clusters in the same area and the Poisson models in different areas, with the largest being defined by a 50% MRCS. Although the sizes of the most likely clusters identified by the Bernoulli models were different, they all had the same cluster period. Based on the sizes of the detected clusters, strict LSD insect-vector control should be undertaken within one kilometer of the outbreak farm in areas where no LSD vaccination has been administered. This study determines the sizes and patterns of LSD outbreak clusters in the dairy farming area with a high degree of farm aggregation. The spatio-temporal study models used in this study, along with multiple adjusted MRCS, provide critical epidemiological information. These models also expand the options for assisting livestock authorities in facilitating effective LSD prevention and control programs. By prioritizing areas for resource allocation, these models can help improve the efficiency of such programs.

Systematic map of the most recent evidence (2010-2019) on ruminant production-limiting disease prevalence and associated mortality in Ethiopia

Background

Ethiopia's livestock sector supports the livelihoods of millions of smallholder farmers. However, despite the improvements of recent years, livestock productivity remains low due to critical constraints, including infectious diseases. The aim of this study was to collate and synthesize the published evidence on ruminant disease frequency and disease-associated mortality in Ethiopia, by identifying knowledge gaps and clusters in the literature to provide the basis for a decision-making tool.

Methods

Searches on both bibliographic and organization databases were conducted in English and were restricted to the period 2010-2019. Search results were screened for relevance at title, abstract and full text level.

Results

There were 716 articles identified relevant to the research question. The systematic map revealed an increased publication output from 2012-2017, compared to 2010-2011 and 2018-2019. Most studies were conducted in Oromia, Amhara and SNNPR. A substantial body of evidence was found for trypanosomosis, ectoparasite infestation, fasciolosis, nematodiasis, echinococcosis, and brucellosis.

Conclusion

This study suggests that despite the high output of epidemiological publications, further understanding of a considerable number of diseases is required and where evidence is abundant, synthesis of information should be carried out in order to better inform decisions on disease control priorities in the livestock sector.

Patterns of mortality in domesticated ruminants in Ethiopia

Background

Premature death of livestock is a problem in all ruminant production systems. While the number of premature ruminant deaths in a country is a reasonable indicator for the nation's health, few data sources exist in a country like Ethiopia that can be used to generate valid estimates. The present study aimed to establish if three different data sets, each with imperfect information on ruminant mortality, including abortions, could be combined into improved estimates of nationwide mortality in Ethiopia.

Methods

We combined information from a recent survey of ruminant mortality with information from the Living Standards Measurement Study and the Disease Outbreak and Vaccination Reporting dataset. Generalized linear mixed and hurdle models were used for data analysis, with results summarized using predicted outcomes.

Results

Analyses indicated that most herds experienced zero mortality and reproductive losses, with rare occasions of larger losses. Diseases causing deaths varied greatly both geographically and over time. There was little agreement between the different datasets. While the models aid the understanding of patterns of mortality and reproductive losses, the degree of variation observed limited the predictive scope.

Conclusions

The models revealed some insight into why mortality rates are variable over time and are therefore less useful in measuring production or health status, and it is suggested that alternative measures of productivity, such as number of offspring raised to 1 year old per dam, would be more stable over time and likely more indicative.

Population, biomass, and economic value of small ruminants in Ethiopia

Ethiopia has a large population of small ruminants (sheep and goats) which are mostly kept in traditional subsistence production systems that are poorly described. Understanding these different systems, their population structure, biomass, production, and economic value is essential for further analysis and effective policy making. The objective of this study was to quantify these parameters for small ruminant production systems in Ethiopia to use them as a basis for analysis of disease burden within the Global Burden of Animal Diseases program. Population structure and trends of small ruminants were analyzed using data from ten annual national agriculture surveys. A stochastic herd model was used to simulate the small ruminant population, biomass, and economic value. The model was parameterised stochastically using data from statistical databases and the literature, and sensitivity analysis of main model outputs to the stochastic inputs was done. Small ruminants are held across the country mainly managed under two major production systems: the crop-livestock mixed system and the pastoral system. The small ruminant population has grown in the past 10 years with an average annual growth rate of 4.6% for sheep and 6.7% for goats. The national average small ruminant population for 2021 was projected at 96.4 (range 95.3–97.7) million heads and the mean stock biomass was about 2,129 (range 1,680–2,686) million kilograms. The monetary value of the small ruminant population was estimated at USD 5,953 (range 4,369–7,765) million. The annual monetary value of small ruminant production outputs was estimated at USD 1,969 (range 1,245–2,857) million. Although the small ruminant population is large and rapidly growing, contributing about 2% of national annual GDP, the sub-sector is characterized by low productivity, low offtake rates, and a limited range of production outputs with no signs of intensification. Efforts should be made to reduce small ruminant mortality, improve fertility, and better utilize products such as milk to improve the livelihoods of rural households and to benefit the national economy. The approaches developed in this study can be replicated in other systems and countries to reveal trends in the size and value of livestock systems, providing a better understanding of its economic importance and performance.