Not included in these estimates were other agricultural acres protected by easements primarily for environmental purposes, such as wetlands, habitat and riparian corridors . Anecdotal evidence suggests that there are many thousands of such acres throughout the state, with easements acquired and held by local land trusts and open space districts, national conservation organizations such as The Nature Conservancy, and state and federal wildlife agencies . Of the 34 organizations, only 24 had acquired agricultural easements by 2000, reflecting that many new programs are still in the formative stage. The programs studied held 29,600 acres in non-agricultural easements and owned outright another 90,900 acres in natural resources and recreational areas, indicating that many programs pursue multiple conservation goals. Only about one-fifth of the total agricultural easement acres were cropland; four-fifths were grazing land. By comparison, cropland and grazing acres represent much larger and smaller shares, respectively, of California’s total farmland base — about one-third and two-thirds of the total . One explanation for the prevalence of grazing acres in easement programs is the relatively large size of cattle ranches and the preference of some programs to put an easement on one large ranch in a single transaction, rather than undertaking the more difficult task of seeking multiple easements from numerous owners of smaller cropland farms. Farms under easement are an infinitesimal share of California’s total 27 million agricultural acres. These are located in only 19 of the state’s 58 counties . They are concentrated in eight coastal counties from Mendocino to the north and Santa Barbara to the south, and in three adjacent Bay Area counties . The coastal counties alone contain more than 80% of all easement acres. Three Central Valley counties are also represented in the easement ranks. This geographical pattern is also notable for the regions that lacked any agricultural easement activity in 2000: the southern and northern thirds of the state. Just as striking is the absence of many important agricultural counties.

Among the 11 counties that lead the state in agricultural production value, each with at least $1 billion in market receipts in 1999,round plastic pots only three contained agricultural easements as of mid-2000 . Counties with farm market values of $1 billion or more in 1999 that did not have agriculture easements were Imperial, Kern, Riverside, San Diego, San Joaquin, Stanislaus, Tulare and Ventura. On the other hand, Sonoma and Marin — the top counties in agricultural easement acres — ranked 16th and 44th, respectively, in farm market value among all California counties. The pattern of varying easement activity among top agricultural counties can be explained, in part, by underlying differences in the conservation sentiments of local populations. For example, Central Coast voters have shown higher levels of support for environmental measures on statewide ballots than voters in inland and southern counties . As a result, citizen coalitions with land conservation or other environmental agendas are more likely to form in Central Coast communities, which may explain the relatively larger number of land trusts in these areas. Yet not all Central Coast counties have active agricultural easement programs. Santa Cruz, Santa Clara and Ventura counties, for example, had recorded no agricultural easements or far fewer than nearby areas as of mid- 2000. In interviews, conservation organization managers cited as reasons the higher costs of acquiring easements on farmland in some of these locations and the greater interest of local land trusts in conserving environmental lands and engaging in educational and other activities.Each of a dozen programs — 11 land trusts and one open space district — had acquired easements on 1,000 or more agricultural acres as of 2000. Their collective holdings totaled about 79,000 acres, 94% of the state total. Among the 12 programs are six coastal programs, three in adjacent counties, two in the Central Valley and one statewide program . The two largest programs combined held more than 46,000 farmland easement acres, or slightly more than half of the statewide total. Serving adjacent North Bay counties, Marin Agricultural Land Trust and the Sonoma County Agricultural Preservation and Open Space District have been the two most active farmland easement programs in California since the mid-1990s. More than 150 separate transactions were represented by these easements. In some cases, the number of separate transactions is a better measure than total acres of the organization’s achievements, since each transaction is the result of a complex process that includes extensive landowner negotiations.

Most of the 12 programs exclusively or primarily hold easements on grazing land. Only the Monterey, Yolo and South Livermore Valley land trusts are exclusively or primarily involved in acquiring and holding easements on cropland, although several others with large holdings in acres overall also have significant cropland acres. The crops grown on easement-protected land include artichokes, strawberries, vegetables and grapes in coastal counties, and field and orchard crops in the Central Valley.The first agricultural easement in California was acquired by MALT in 1983. In fact, most of the easements accumulated by all agricultural programs by 2000 had been acquired only in the previous 5 or 6 years, since the early and mid-1990s. The 12 leading programs quickly established successful records. Other programs in our sample had accomplished far less or had yet to acquire their first agricultural easements, although some had placed significant amounts of non-agricultural land under environmental easements. Each of the leading programs followed a unique path, but they have several underlying factors in common. The most critical element seems to have been early access to funds or other acquisition resources, in part a result of fortunate timing, location and community support. But success was also the result of the skill and persistence of program staff and community leaders in working with landowners and putting together the resources to complete easement deals.The coastal location of four programs gave them access to state conservation funds earmarked for this region. California state government for several decades has targeted the long, narrow Pacific coastline for special conservation measures, starting with the creation in 1972 of the Coastal Commission, a land use regulatory agency. A more beneficial action for landowners was the formation in 1976, by state legislation, of the Coastal Conservancy, which was given the non-regulatory task of preserving coastal areas through landowner compensation. State appropriations began to flow to local agencies after the 1988 passage of Proposition 70. This bond issue generated $776 million for state and local land conservation programs, mainly to acquire and improve parks and wildlife habitat. A small portion of the total, some allocated through the Coastal Conservancy, eventually was used to acquire easements on farmland that had other resource values .

At least four of the successful programs benefited from these actions shortly after their formations. MALT, the Sonoma Land Trust and the Monterey County Agricultural and Historical Land Conservancy, either independently or with their county governments, each received a $1 million grant from the Coastal Conservancy in the mid-1980s. All three land trusts, together with the Peninsula Open Space Trust , a few years later obtained Proposition 70 funds for easement activities. Two land trusts among the 12 leading agricultural programs have prospered from a different type of funding stimulus — local government mitigation of farmland loss, which requires urban development projects to pay for easements on comparable farmland. The Yolo Land Trust received its initial easements as the result of a mitigation ordinance passed by the city of Davis in 1995,the first such municipal action in California. The South Livermore Valley Agricultural Land Trust was formed and acquired many of its easements as the result of litigation,hydroponic bucket in which the city of Livermore successfully challenged a large residential project. Another trust that has concentrated on environmental easements, the Solano County Farmland and Open Space Foundation, also benefits from a mitigation arrangement as a result of litigation involving the city of Fairfield. The Sonoma County Agricultural Preservation and Open Space District is in a different funding category because of its countywide 0.25-cent sales tax, approved by voters in 1990. The district, which relies exclusively on this 20-year revenue source for its operations and purchases, was the only California entity with a dedicated tax for acquiring farmland easements until November 2000, when voters in Davis approved a parcel tax to fund a land conservation program that includes easements.Several of the other leading programs depended largely or primarily on landowner donations of easements, including Napa County Land Trust, Land Trust for Santa Barbara County, California Rangeland Trust and Sonoma Land Trust. All four have broader open space and conservation interests than just farmland protection, and their donated easements have come primarily from owners of large ranches who are generally motivated by the tax benefits or preservation of the environmental qualities of their properties. Beyond their formative years, most of the successful programs have been able to tap a variety of sources to fund easement acquisitions . External sources and landowner donations of easements were most frequently used, particularly grants from state and federal governments and from private foundations. Internal sources, including both private funds and public revenues, were less widely used. California’s strict rules for funding local government programs, especially the restricted property tax and two thirds voter approval requirements for new or increased taxes, severely limit the ability of communities to support easement programs with local taxes.The less tangible elements of program entrepreneurship were also important to the success of the leading programs. Even with early funding opportunities, the leaders of the better-funded programs had to apply skill, focus and persistence, and to work hard over long periods of time, to get their organizations going.

To build a record of acquisitions, they had to look for funds from competitive sources and/or seek landowner donations. Program leaders had to match funding with landowner interests, a process that involved a time sensitive juggling of several interrelated factors: dealing with foundations and state government as funding sources, selecting or seeking out appropriate parcel candidates according to the trust’s priorities, matching available funding with available landowners and negotiating with landowners. Each easement transaction is process unto itself. One land trust manager estimated that the average direct cost of completing a transaction was $15,000 in staff time and other expenses. Another estimated that putting an easement together required several hundred hours in staff time. As much as funding or landowner donations, the critical resources in this process were the personal skill, focus and persistence of program leaders. The successful programs generally have small professional staffs, but they also rely extensively on the work of volunteer boards, which typically include members with expertise in law, resource management, agriculture, finance, land appraisal and other relevant areas. Land trusts with boards composed substantially of farmland owners, such as the Yolo and Merced trusts, are especially equipped to use board members’ local knowledge and personal contacts in persuasive discussions with other farmers and ranchers who own desirable parcels. Explaining this strategy, one land trust manager noted: “The [founders of the trust] knew that they couldn’t be an urban group going out to the farmers and telling them how to change their land. The message had to come from colleagues — other farmers and ranchers in the community.If you want to be effective in agricultural preservation, you need to get along with the people of the land. . . and that includes the Farm Bureau” .Despite recent achievements of the leading programs and general popularity of this non-regulatory technique for preserving farmland, agricultural easement programs have not yet caught on in a substantial way in most of California’s agricultural counties. The limited progress so far may be understandable, considering the still-new status of the technique as a farmland protection tool. In time, with greater familiarity and acceptance in agricultural and other community circles, and fueled by the achievements of the first active programs, there could be a rapid expansion in the number and geographical extent of programs and acres covered . This is not a certain scenario in the near future, however.It takes a high degree of citizen interest or local government support to form a land trust or open space district, qualities not currently present in many agricultural areas. Local and regional organizations run the easement programs, rather than more distant state government and other outside agencies, because successful easement transactions depend on close relations with landowners.

Geomembranes are generally welded in the field.The seam is considered a critical point for possible injuries and future leaks in the barrier system.Rollin et al.reported that the total number of faults in geomembranes occurred in landfills, ponds and basins, and 55% of them were found at the seams.Stark et al.evaluated seams made by industry and in the field of a polyolefin geomembrane for a water reservoir project.According to the authors, the seams made by industry can minimize dirt and moisture in the seam, ambient temperature changes and wind.After good statistical data work, the results of this research showed that the seams made by industry were 9% stronger than field seams for peel strength and 10% stronger for shear strength.Moreover, seams according to industry data showed less variability.Lavoie et al.evaluated two different HDPE geomembrane samples exhumed from a municipal landfill leachate pond and a sewage treatment aeration pond.The authors conducted thermal, physical and mechanical analyses.The thermogravimetric analyses showed an altered decomposition behavior for the landfill leachate pond sample, probably caused by the interaction with the leachate.The sewage pond sample presented low stress cracking resistance and low tensile elongation at break, compatible with the dynamic mechanic analysis , which showed an increase in the stiffness.Ewais and Rowe studied an HDPE geomembrane with 1.0 mm of the thickness of about five years immersed in synthetic leachate at different temperatures and in air and water at 55 ◦C.The authors observed changes in the sample’s stress crack resistance values before chemical degradation and before antioxidant depletion for temperatures lower than 70 ◦C.The major SCR value decrease was noted for the sample immersed in leachate at 55 ◦C, reaching 26% of the SCR value of the virgin sample.

This behavior was attributed to morphological changes during aging that affected the interlamellar connections due to the annealing that increased the strength of the inter-lamellar connections and the proposed chain disentanglement mechanism.HDPE geomembranes are often utilized as a flow barrier in landfills, mining facilities, canals, waste liquid ponds,mobile vertical farm and farm ponds.This research evaluated three exhumed high-density polyethylene geomembrane samples from two different shrimp farming ponds after 8.25 years of field exposure for the first pond and 3.0 years of field exposure for the second pond.Thermoanalytical and physical analyses were used to understand the final conditions of the HDPE geomembrane samples.Analyses such as thickness, density, carbon black content, melt flow index , tensile properties, stress crack resistance, oxidative-induction time , thermogravimetry,differential scanning calorimetry,and dynamic mechanical analysis were performed.Using an HDPE geomembrane as a liner of a shrimp farming pond is limited to some places globally, such as the northeast region of Brazil.This work aims to provide new data on the HDPE geomembrane performance in shrimp farming pond applications.This work evaluated three high-density polyethylene geomembrane samples exhumed from two different shrimp farming ponds in the northeast of Brazil with 0.8 mm of nominal thicknesses.The samples called “CAM” and “CAM1′′ represent the same geomembrane installed in a shrimp farming pond and were collected after 8.25 years of field exposure.The CAM sample was exhumed from the bottom liner and had been in contact with the salinized water.The CAM1 sample was exhumed from the same pond as the CAM sample, but it was taken from the slope liner and had been in contact for 8.25 years with environmental conditions.The third sample was exhumed from another shrimp farming pond after 3.0 years of service.This pond has the particularity of being covered with an agricultural plastic film.Figs.1 and 2 show, respectively, the shrimp farming pond representing the CAM and CAM1 samples and the shrimp farming pond representing the CAM2 sample.Table 1 presents the salinized water parameters used for shrimp cultivation.

However, nesting habitats and foraging resources for pollinating insects have decreased severely due to intensification in agricultural landscapes.Developing effective mitigation tools to counteract pollinator losses is essential for satisfactory pollinator diversity and sustainable agriculture.In Europe, agri-environment schemes are among the most valuable instruments to reduce the pressure of agriculture on the environment.The new Common Agricultural Policy of the EU also aims at increasing their share by targeting one-fourth of the direct payments to subsidise eco-schemes.AES offer two main tool types to support biodiversity and thus pollination, i.e.productive and non-productive schemes.Organic farming is one of the most favoured productive management systems where farming intensity is reduced with a preference for mechanical methods over agrochemicals.In their meta-analysis, Tuck et al.found that organic farming increased overall species richness by 30 %, with plants benefiting the most.Pollinator insects also profit from organic farming.Flower strips are flower-rich non-crop landscape features created at arable field edges.The annual or perennial strips are formed to enhance invertebrate pest control and pollination by providing forage, shelter, and nesting places.As AES schemes differ in concept, they should be evaluated against each other to prioritise them for benefits to biodiversity.Functional trait-based assessments offer an effective tool for studying community functionality, redundancy and stability in agroecosystems.Different management types can support various arthropod taxa by maintaining specific plant functional traits and high functional trait diversity.Plant traits associated with visibility, quantity, and quality of floral resources are the most important traits shaping pollinator communities.For example, plants with large, conspicuous inflorescences attract pollinators efficiently , while floral patterns resulting from UV reflection are also important signals to several major pollinator groups.Nectar is the primary reward for pollination; therefore, spatial and temporal variation in nectar volume influences pollinator behaviour and survival.As land-use significantly affects pollination-related flower traits , their diversity can indicate the quality and quantity of floral resources and can be used to evaluate AES schemes for pollinators.Different field parts with different management regimes can support biodiversity and pollination to varying degrees.The interior of arable fields covers the largest area of agricultural landscapes; therefore, its ecological condition may be decisive for biodiversity at the landscape scale.Intensive management within field interiors favours plants with ruderal strategies , and selects for species with early flowering and selfing.Crop edges are also shown to be keystone transitional zones for pollinators and support increased plant diversity compared to field interiors as they are expected to contain both typical arable weeds and species originating from grassy margins.

Linear non-crop landscape elements such as grassy margins along field edges are less intensively managed than field interiors and also provide important shelter and forage resources for various taxa in agricultural landscapes.The overall performance of AES schemes on pollination should be quantified by assessing all characteristic parts of the agricultural fields.Landscape-scale factors should also be considered beside local factors when studying the effects of AES schemes on pollinators and their services.The effectiveness of AES varies by landscape heterogeneity that has two main components.Compositional heterogeneity describes the relative amount of habitats in the landscape, whereas configurational heterogeneity refers to their spatial arrangement.Most studies assessed the effect of landscape composition on pollinator communities and found that pollinator abundance and richness were higher in landscapes comprising more high-quality habitats.The role of landscape configuration is less studied, though it may also be very important as landscapes with smaller fields have higher structural connectivity and more edges providing more floral resources for pollinators.Given this research gap, here we focus on the role of the less studied configurational heterogeneity by assessing landscape-scale mean arable field size.Our study presents a trait-based approach to estimate the potential of two agri-environmental schemes to support pollinators based on floristic composition.For this, we studied plant traits associated with pollinator attraction and reward accessibility.We calculated community-weighted trait means and functional trait diversity in organic and conventional winter wheat fields complemented with sown flower strips and compared them to conventional fields along a gradient of landscape configurational heterogeneity.We tested whether management intensity and landscape heterogeneity select for specific pollination-related plant traits, and result in differing trait diversity by using literature data on species traits.Our specific hypotheses were the following: Pollination-related plant trait diversity is reduced with increasing mean field size within the landscape.Flower strips and organic farming maintain functionally more diverse plant trait composition than control conventional fields.Pollination-related plant trait diversity decreases from grassy margins to field interiors in both conventional and organic fields.We considered only insect-pollinated plant species that may attract pollinators and provide nectar resources.We assessed the following traits: flower size, flower colour, UV pattern in the flower,vertical farming racks reward quantity, and flowering duration.Trait values were collected from the BiolFlor database and identification books.Flower size was expressed as flower diameter.For zygomorphic flowers, the mean of the longest and shortest diameter was calculated.In the case of compact inflorescences that can be regarded as one functional pollination unit, we used the size of the whole inflorescence.We expressed flower colours on a continuous wavelength scale.White-flowered species got the mean value of the visible light spectrum.The presence of UV pattern in the flower was treated as a binary variable.In the case of reward quantity, we considered only nectar resources because of no available information about pollen resources for the majority of the species.Nectar quantity was provided on an ordinal scale with four categories.

The average flowering duration was coded as a continuous variable.For each transect, we calculated the total number and average percentage cover of insect-pollinated plant species.We quantified transect-level community-weighted means for each trait by averaging trait values weighted by species abundance.We tested the effects of management type , transect position , mean field size on the species richness and cover of insect-pollinated plant species, Rao’s quadratic entropy and the CWM and FD values for each trait by using linear mixed-effects models.We used generalized mixed-effects models for species richness with Poisson error distribution and log link function.For each response variable, we started with a model with all two-way interactions of the fixed effects and then selected the minimum adequate model from all possible nested models with the minimum Akaike Information Criterion value.The effect of years was not assessed per se but was treated as a random effect in the analyses.The field, farmer, and landscape identities were also used as random effects nested within each other and crossed with year.We used log transformation for cover values and square root transformation for CWM and FD values if necessary to fulfil test assumptions.We restricted our assessment on those 151 transects that harboured at least one insect-pollinated plant species for flower colour analyses to have meaningful results.We derived marginal and conditional coefficients of determination of the models to estimate the variance explained by the fixed effects and the entire model including both fixed and random effects using the MuMIn.We conducted multiple GLMM tests, therefore, we controlled for the false discovery rate.Post hoc pairwise comparisons were made with the em means package , and were presented in the figures.All analyses were conducted in the R environment.We found that flower strips and organic farming outperformed conventionally farmed fields, as they had the highest insect pollinated plant species richness and supported higher trait diversity favourable for pollinators.Flower strips were superior with the highest cover of insect-pollinated plants and the highest ratio of flowers showing UV patterns.Although annual flower strips doubled the insect-pollinated plant species richness in the grassy margins, the benefits of flower strips completely diminished in neighbouring field interiors.In contrast, organic field interiors could be considered the most favourable pollinator habitat among the studied field interiors based on plant pollination traits.Thus organic farming can enhance pollinators at a larger spatial scale with more environmentally friendly crop production on the whole area of the arable land.We did not find any effect of configurational heterogeneity expressed as mean arable field size.We showed that organic farming outperformed conventional farming practices regarding insect-pollinated species richness, flower attractiveness, and food resources provided for pollinators.This is primarily due to lower overall farming intensity in organic fields.Even organic field interiors offer abundant and diverse flower resources to pollinators compared to conventional farms.Thus, we can consider organic farming the most beneficial method based on pollinators’ resources.In contrast, intensively managed conventional fields supported the lowest richness of insect-pollinated plant species showing that only a few plant species could establish here randomly.Annual flower strips sustained the most favourable vegetation for pollinators, considering the quantity and quality of flower resources.They had six times higher cover of insect-pollinated plants than the edges of the other two farming methods.

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.