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Small Medium and Micro Enterprises (SMMEs) play a significant role in the socio-economic stimulation of South Africa. Particularly, South African SMMEs are believed to contribute around 50% to the national Gross Domestic Product (GDP) while simultaneously employing up to 80% of the national workforce. Prior research does however suggest that these business entities have among the worst sustainability rates in the world as up to 75% fail after being in operation for three years. A probable reason for the latter dispensation is the non-management of economic factors, one of which is that of Taxation. Taxation is a mandatory obligation imposed on citizens of a country to fund state expenditure. For this working paper, Customs and Excise Taxation is focused on; Taxation that is levied on goods and/or services that are hazardous to both human- and environmental health and wellness. Over the years, Customs and Excise Taxation has not only been imposed on tobacco products, alcoholic products and plastic bags but have also increased year-on-year, to date. As such, the perception was formulated by the author that the sustainability of South African (retail) SMMEs, especially those who sell tobacco products, alcoholic products and plastic bags, are adversely affected by the year-on-year increases of Customs and Excise Taxation. The primary objective of this working paper is to ascertain whether the perception formulated shows truth, in theory. This working paper therefore takes on a non-empirical, exploratory approach that makes use of a qualitative research methodology. Stemming from the findings, thus far, it appears that Customs and Excise Taxation adversely influences the sustainability of South African SMMEs that sell tobacco products, alcoholic products and plastic bags.

Inland valleys are becoming increasingly important agricultural production areas for rural households in sub-Saharan Africa due to their relative high and secure water availability and soil fertility. In addition, inland valleys are important as water buffer and biodiversity hot spots and they provide local communities with forest, forage, and fishing resources. As different inland-valley ecosystem functions may conflict with agricultural objectives, indiscriminate development should be avoided. This study aims to analyze the diversity of inland valleys in Sierra Leone and to develop guidelines for their sustainable use. Land use, biophysical and socio-economic data were analyzed on 257 inland valleys using spatial and multivariate techniques. Five cluster groups of inland valleys were identified: (i) semi-permanently flooded with good soil fertility, mostly under natural vegetation; (ii) semi-permanently flooded with very low soil fertility, abandoned by farmers; (iii) seasonally flooded with low soil fertility under low input levels, used for rainfed rice and off-season vegetables for household consumption and market; (iv) well drained with moderate soil fertility under medium input levels, used for rainfed rice and off-season vegetables for household consumption and market; and (v) well drained with moderate soil fertility under low input levels, used for household consumption. Soil fertility, hydrological regime, physical and market accessibility were the major factors affecting agricultural intensification of inland valleys. Opening up the areas in which inland valleys occur through improved roads and markets, and better water control through drainage infrastructures along with an integrated nutrient management would promote the sustainable agricultural use of inland valleys.

Yield data collated from the Global Yield Gap and Water Productivity Atlas (http://www.yieldgap.org/) were used to compute this sensitivity index. In the case of Nigeria yield data were available for the period between 2002 – 2010. The index was computed using the method described by Shi and Tao (2014). This method involves detrending (multiplicative detrending method) the yield data and this led to the generation of expected yield for each year. The index was then computed as the ration of expected Maize yield divided by the actual linear yield for this period. Detrending has been reported to aid the removal of the potential impact of technology, error in reporting and captures the variations in yield attributable to climate. The computed index can be linked to a GIS file (Shapefiles) availble from the Global Yield Gap and Water Productivity Atlas to create a visual representation of the index. High index value indicate high yield sensitivity.

The data used in this article are related to the rice yield following the use of the RiceAdvice technology recommendation and the rice yield following the recommended level of fertilizer recommendations. The data were collected in 5 sites in Mali. In all sites, rice yield with RiceAdvice was higher than rice yield following the conventional levels of fertilizer. On average, rice yield with RiceAdvice was 0.7 t/ha higher than rice yield with the conventional level of fertilizer recommendations.

The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.

The potential impact of the ICT, Food and Agriculture and Finance sector on children in Serbia and opportunities for shared valued partnerships with organisations and institutions supporting children and their rights. The major impact area for all three examined business sectors is the marketplace, from ensuring that products and services are safe to children and seeking to support children’s rights through them, and in using marketing and advertising that respect and supports children and their rights. The mapped key impact area creates related risks, which are also associated with the use and availability of products and services, their responsible promotion, and environmental risks posed to the local community. The opportunities for shared value partnerships for all three sectors can be found in advocacy initiatives on raising awareness on perceived risks that children could be exposed to using companies’ product and services and agenda-setting through influential sectoral platforms. Besides, child rights consideration should be integrated into all appropriate corporate procedures and committed by management processes. When it comes to financing, it should be covered from a broader angle, from strategic grants and contribution in-kind to the engagement of employees in terms of their time and expertise, and customers around the cause of the support and promotion of children’s rights in Serbia. Finally, a clear untapped potential can be found in innovative products and services and the expertise of the leading companies in the sector, which would make their offer more inclusive and impact on the overall well-being of children, thereby creating a business contribution to the Sustainable Development Goals.

This data set provides detailed numerical data on the Life Cycle Inventory (LCI) analysis for assessing and comparing the environmental impact related to the treatment of sewage sludge (SS) in open windrow facilities for the production of compost and in an anaerobic digester for the production of biogas. The collection of the data is included in eleven tables, in which the machinery and duration for biogas and compost production procedures, as well as comparative data regarding the environmental impact category emissions for both scenarios are shown . Furthermore, electricity consumption and embodied energy for each scenario compared to other studies are given. The system boundary conditions were determined with the use of the Gabi software.

Multispectral data from two legume-grass mixtures (clover- and lucerne-grass) were collected in the year 2018 for aboveground biomass and nitrogen fixation (NFix) estimation. In addition to the mixtures, pure stands of legumes and of grasses of the two mixtures were sown in order to represent variable conditions in practical farming (0-100% legumes). All six treatments were cultivated in four replicates and harvested three times within the year (plot size: 1.5 x 12 m). Destructive biomass samples for fresh (FM) and dry matter (DM) and NFix determination were taken three times at harvest. To cover the entire vegetation season, sub-sampling for DM and FM was done five times between the harvests. Flight missions were carried out one day before each of the eight sampling dates. A multispectral sensor (Parrot Sequoia, MicaSense Inc, Seattle, USA) with four spectral bands (green, red, red edge, near infrared) was mounted on a low-cost unmanned aerial vehicle (UAV; DJI Phantom 3, Advanced, Shenzhen, China). Eight black and white ground control points (GCPs) were distributed in the pathways. Coordinates of the plot corners and GCPs were measured by a Leica real time kinematic global navigation satellite system (Leica RTK GNSS). Orthomosaics were created by the overlapping images with a photogrammetric processing software (Agisoft PhotoScan Professional, Agisoft LLC, St. Petersburg, Russia). The orthomosaics were georeferenced using the coordinates of the GCPs. The mean reflectance value of the four bands was extracted by zonal statistics in QGIS (Quantum Geografic Infromation System) using the four plot corners of each plot as boundaries. Furthermore, eight texture features of every band were calculated, provided by the processing tool HaralickTextureExtraction of the Orfeo Toolbox library (OTB) in QGIS.