We assessed that the first 50 publications contain sufficient information for answering the research questions and thus were used as exclusion criteria. The primary studies with their authors and publication year are included in the appendix . To get insight into the relevance of the topic for this SLR, the results of the broad and narrow search results are grouped by year of publication and presented in Fig. 1 and Fig. 2, respectively. When looking at the number of primary studies published for the broad search query, it seems that there are downward trends in 2010 and between 2014 and 2018. This is however due to the high number of publications that were found and the exclusion criteria that affected publication in those years disproportionately. Many publications were excluded because they were outside of the 50 most relevant publications. However, there is a consistent and sharp increase in the number of publications since 2018 both for the narrow and broad search queries. To get insight into the focus of the primary studies, we have categorized the studies by topic . The categories clearly indicate that majority of the studies were on pig health. Most studies focussed on the presence, spread, or outbreak of diseases, and few studies focussed on the causes and prevention of boar taint. In the selected primary studies, pig health and the performance of pig production were researched often. The production performance was investigated by analysing various parameters, such as the DNA of pigs,rolling bench fat composition of pig meat, meat quality, and muscle thickness. This shows that the production performance of pig farming was mostly measured using the attributes of the meat measured after the animals are slaughtered. Besides pig health and pig production results, other topics were researched.

Several feeding strategies have been developed and evaluated. Further, research was done to test if the origin of meat could be traced and on-farm environmental influencers, such as disease spread in manure and temperature effects on pigs. The narrow search query focused more on the application of ML and the topics of research are summarised in Table 7. According to the search results, most of the research focussed on performance related issues, such as pig performance, feeding strategy, and genetics. Pig health is the second most researched area, focusing mainly on diseases. The trend of using ML for disease detection through image and video analysis is reflected in the number of primary studies found on this topic. Pig detection and behaviour analysis were also performed using ML. Feeding strategy was analysed using basic statistical analysis and using ML. There were also primary studies that focus on detecting the origin of meat, detecting stress in pigs. A detailed description of the topics of research per primary study is shown in Appendix B. As can be seen in Table 8, a variety of data were used for research in the pork industry. Data were largely collected from body and tissue . The rest of the data reported refers largely to measurements of the external characteristics of the animals and their living environment or feed . There were also primary studies that used data on manure and micro-arrays. The type of data used per primary study is shown in Appendix E. The results of the analysis of data from the second search query focused on the application of ML to pig farming are shown in Table 9. The table shows the widely used features used in research. The features used can be grouped into features on pigs and features on climatic conditions.Pork meat information, images and recordings, growth, occupancy and reproduction, genetics, and pig movement have been used to build and validate models. The features on climatic data consist of data about indoor or outdoor climatic conditions, or both. Data used in those primary studies consist of outside temperature, indoor temperature, and relative humidity. The features used per primary study are shown in Appendix H.

The location of data gathering provides insight into the main sources of research data. We have categorized the locations where data are gathered into groups , which shows that most of the data is gathered at farms and slaughterhouses. The farms were sometimes conventional farms where a specific group of animals was monitored closely; other times, they were experimental farms used for precisely monitoring growing conditions and pig behaviour. Slaughterhouses serve as one of the main data gathering locations. The quality and composition of meat are important indicators of production performance; therefore, data from slaughterhouses where these indicators are routinely measured, are important sources of data. Other locations of data gathering for research include laboratories , externally collected data, previous research, or large database where data are shared. Further, data was collected at distribution centres, urban abattoirs, and there was a case of data from a virtual simulation. The data gathering location per primary study is shown in Appendix C. To get an overview of data analysis methods used in the primary studies, the algorithms or principles used were calculated as shown in Table 11. Least squares regression was used most often, followed by logistic regression. The primary studies found by the broad search query did not often use complex algorithms; instead, they applied variations of the linear regression model. All data analysis methods used are shown in Appendix F. All primary studies found by the second search query used ML algorithms. Random forest algorithm was used most, followed by neural networks and support vector machine, as is shown in Table 12. Compared to the primary studies found by the broad search query, those found by the narrow search query used more complex analysis models.

The algorithms used per publication are shown in Appendix G. Eight studies using models based on random forest and/or neural networks recorded an R-squared accuracy scoring higher than 0.7. The challenges stated include difficulty in scaling into large-scale farms due to differences in the environment between the experimental setup and large scale farm, and other features that influence the performance. Table 13 shows what has been studied, and if the research subjects were pigs, it shows the size of the group of pigs studied. A total of 26 primary studies considered live pigs, from which measurements were made. The number of pigs used for research varies significantly, from 5 to 908,582 pigs. Three primary studies considered farms as a whole, focusing on the growing environment of the pigs. Further, data on meat quality and faecal samples were used by some studies. There were also studies that used externally collected data, virtual experimental data, RNA pools, and liver pasts. Appendix D shows the research group types and sizes per primary study. Much research has been using data on genetics, pig breeds, meat quality after processing, disease recognition, and factors influencing diseases. Research that focused on the prevention of negative aspects of the final product is, for example, research done on boar taint and the attempt to reduce it. Boar taint in meat results in a lower price for the carcass. A variety of challenges have been stated in the studies regarding ML. The largest challenge is making the algorithms perform consistently high for all situations. Many studies reported that it was feasible to define a well performing model for the specific research setup and condition the model is trained and tested on but it was in general not possible to guarantee the same level of performance in practice. Mostly, more data and more features are required in order to improve the overall performance of the models developed.The original aim of this study was to review the literature on the use of advanced data analytics techniques in real-life business scenarios. We tried diverse search strings in order to find studies that are not based on experimental set-up be based on the use of data sources from real-life production chains. But the number of results we obtained was low.

We were unable to find any study that fused data from the whole production chain except one by Ma et al. . Ma et al. developed an intelligent feeding equipment and network service platform,roll bench using sensors, software, and monitoring units. This system helped in optimizing feeding methods and improving pig management. We considered, therefore, two search strings, a broad and a narrow search string: the former focussing on general data analytics and the second on machine learning. In both cases, we used more generic terms and relied on exclusion criteria to select the relevant studies. We consulted five scientific databases in this SLR, namely: Science Direct, Scopus, Web of Science, Springer Link, and Wiley. We did not consult Google Scholar initially which returned 16,300 publications, a vast majority of which were irrelevant, and thus filtering out the relevant publications was infeasible. The exclusion criteria we selected ensured that only publications that are accessible and of high quality were considered. The exclusion criterion on year of publication was based on our initial test search results. While research performed 20 years back is likely outdated, the set year allowed us to get a clear view of the trend in the studies published. This made it clear, for instance, that the number of primary studies increased substantially from 2018 onwards, particularly on the application of machine learning in the pork sector. When analysing the results of the SLR, we grouped the attributes such as features, data types, or algorithms we found. Grouping the results allowed us to derive clear conclusions. The detailed results are however included as appendices to this study. As can be seen in the increasing number of primary studies since 2018, the application of ML will be important in the pork sector in the future. When looking at the algorithms used in this context, studies that used features and data on images and recordings often used neural networks, specifically convolutional neural networks algorithm. For example, Cowton et al. , Mukherjee et al. , and Fernandes et al. focused on pig recognition and deviations in behaviour. Studies that used pork meat information often had random forest as best performing algorithm. The focus of the research using pork meat information was to investigate growth performance or the state of health .

What is striking in our review is that most of the research conducted use only groups of pigs selected for an experiment. Most of the studies are conducted in large pig farms; however, the numbers of animals studied within the farms were often small, indicating the studies were conducted on an experimental basis and not on the entire herd. While the focus on a selected group of pigs helps to accurately track and monitor pigs, it does not reflect the real-life situation. Therefore, it remains unclear whether the solutions found in research could be applied in real-life business cases. Out of the 41 primary studies we considered through the broad search query, only 10 involve more than 500 pigs, and of which only two, performed by Naatjes et al. and Haraldsen et al. , used real-time data from farm management systems. Research based on real-life data is scarce and most studies focussed mainly on isolated and one-off problems. We suggest that future research should consider the complexity encountered in real-life circumstances and the integration of data analytics within the management systems so that the analytical results can be used within routine business processes. The Republic of Botswana has an installed capacity of 550 MW with an estimated population of 2.3 million of inhabitants. It is reported that the country has one of the highest national electrification rates in Africa determined as 60 % with 77 % of the population connected to electricity in urban areas and 37 % connected in the rural areas. According to Statistics Botswana, it is stipulated that the Government has set national electricity access target of 82% by 2016 and 100 % by 2030. With an average energy demand of more than 400 MW, Botswana relies mainly on fossil fuels energy sources such as coal, fuel wood, and petroleum and an imported power from South Africa to meet needs of the population. Additionally, the country is also known for its particularity in renewable energy sources such solar, wind and various forms of bio-energy such as bio-fuels and biomass wastes.