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Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c08e08aa-3055-4651-801b-0383610c1987}.

Urea is the most common nitrogen (N) fertilizer in agriculture, due to its cheaper price and high N content. Although the reciprocal influence between NO3- and NH4+ nutrition are well known, urea (U) interactions with these N-inorganic forms are poorly studied. Here, the responses of two tomato genotypes to ammonium nitrate (AN), U alone or in combination were investigated. Significant differences in root and shoot biomass between genotypes were observed. Under AN+U supply, Linosa showed higher biomass compared to UC82, exhibiting also higher values for many root architectural traits. Linosa showed higher Nitrogen Uptake (NUpE) and Utilization Efficiency (NUtE) compared to UC82, under AN+U nutrition. Interestingly, Linosa exhibited also a significantly higher DUR3 transcript abundance. These results underline the beneficial effect of AN+U nutrition, highlighting new molecular and physiological strategies for selecting crops that can be used for more sustainable agriculture. The data suggest that translocation and utilization (NUtE) might be a more important component of NUE than uptake (NUpE) in tomato. Genetic variation could be a source for useful NUE traits in tomato; further experiments are needed to dissect the NUtE components that confer a higher ability to utilize N in Linosa.

Abstract Castor seed is an important oil seed crop of dryland agriculture. To estimate the energy needs of this crop, two experiments were conducted with three tillage treatments under two farming systems, namely bullock and tractor farming. Under all the selected tillage treatments both for tractor and bullock farming systems, it was found that threshing, harvesting, manual weeding and seed bed preparation were the energy intensive operations. No-tillage treatments was found not only the least energy consuming package of practices but also resulted i n a comparatively high output-input energy ratio despite higher weed intensity. Further, tractor cultivation consumed a higher amount of energy as compared to bullock cultivation but did not result in higher production. The farmer may, therefore, continue with bullock cultivation unless he were to cover larger areas which cannot be commanded by the bullock power resources available to him.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three ‘Tier 2’ indicators (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under cover crops, grassland cover, organic amendments and residue return compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and December 2017 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control (conventional cropping with that found under soil health promoting practice) and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include key descriptor variables and covariates useful for development of scoring functions which include: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class and pH, 3) depth of sampling, 4) analytical methods for quantification (i.e.: loss on ignition, combustion), 5) units used in published works (i.e.: equivalent mass, concentration), 6) SOC class (L,M,H), and 7) statistical significance of difference comparisons.

This dataset contains measures of fitness traits from Eschscholzia californica progeny which were experimentally supplemented with selfed or outcrossed pollen to determine the effects of self-fertilisation on a plant which has a low propensity to self. A glasshouse experiment was conducted using 40 plants. On each plant two flowers were emasculated and the first supplemented with outcrossed pollen and the second with self-pollen. From each supplemented plant, a seed was sowed from the outcrossed fruit and from the selfed fruit. The following fitness traits were recorded; the germination rate, the duration from germination to reproductive maturity (time of first flower), together with the height (cm) and biomass (number of flowers and buds) at reproductive maturity. The dataset was part of a larger experiment looking at the effect of floral resources on the pollination services to isolated plants. We performed a glasshouse experiment using 40 artificially crossed plants. On each plant, we emasculated two flowers and supplemented the first with outcrossed pollen and the second with self-pollen. This involved methodically wiping two dehiscing anthers from a donor plant or the focal plant onto the receptive stigma with dissecting tweezers, before covering it in fine muslin. From each supplemented plant, we sowed a seed from the outcrossed fruit and from the selfed fruit (given that selfed fruits predominantly only produced one seed) into 1L pots. These were then stored under glasshouse conditions before the fitness traits were measured.

1.Perennial bioenergy systems, such as switchgrass and restored prairies, are alternatives to commonly used annual monocultures such as maize. Perennial systems require lower chemical input, provide greater ecosystem services such as carbon storage, greenhouse gas mitigation, and support greater biodiversity of beneficial insects. However, biomass harvest will be necessary in managing these perennial systems for bioenergy production, and it is unclear how repeated harvesting might affect ecosystem services. 2.In this study, we examined how repeated production-scale harvesting of diverse perennial grasslands influences vegetation structure, natural enemy communities (arthropod predators and parasitoids), and natural biocontrol services in two states (Wisconsin and Michigan, USA) over multiple years. 3.We found that repeated biomass harvest reduced litter biomass and increased bare ground cover. Some natural enemy groups, such as ground-dwelling arthropods, decreased in abundance with harvest whereas others, such as foliar-dwelling arthropods increased in abundance. The disparity in responses is likely due to how different taxonomic groups utilize vegetation and differences in dispersal abilities. 4.At the community level, biomass harvest altered community composition, increased total arthropod abundance, and decreased evenness but did not influence species richness, diversity, or biocontrol services. Harvest effects varied with time, diminishing in strength both within the season (for total abundance and evenness), across seasons (for evenness), or were consistent throughout the duration of the study (for community composition). Greater functional redundancy and compensatory responses of the different taxonomic groups may have buffered against the potentially negative effects of harvest on biocontrol services. 5.Synthesis and applications. Our results show that in the short-term, repeated harvesting of perennial grasslands (when insect activity is low) consistently altered vegetation structure but had mixed effects on natural enemy communities and no discernable effects on biocontrol services. However, the long-term effects of repeated harvesting on vegetation structure, natural enemies, and other arthropod-derived ecosystem services such as pollination and decomposition remain largely unknown. Kim et al. 2017 Harvest effects on natural enemy communities and biocontrolData summary tables and site information used in Kim et al. 2017. Harvesting biofuel grasslands has mixed effects on natural enemy communities and no effects on biocontrol services. Journal of Applied Ecology.Kim et al-Harvest effects on natural enemy communities and biocontrol JAE.xlsx

This dataset includes the results summary from a lab-scale bioreactor experiment as discussed in the research paper with the same name, published at Processes MDPI (De Groof, V.; Coma, M.; Arnot, T.C.; Leak, D.J.; Lanham, A.B. Adjusting Organic Load as a Strategy to Direct Single-Stage Food Waste Fermentation from Anaerobic Digestion to Chain Elongation. Processes 2020, 8, 1487.). The study comprised two operational phases of duplicate reactors fed with food waste, each set to target a different product. The data comprises a summary on feedstock composition, microbial community analysis and operational conditions and product outcome per operational phase. The archaeal and bacterial community data includes the final sequences of the operational taxonomic units found and their relative abundance in each sample as determined by 16s rRNA amplicon sequencing. The raw data files have been submitted in the specialized EMBL-EBI database and are available under the accession number PRJEB39281. This dataset was prepared and processed in Microsoft Excel from raw analytical data. The bioinformatic processing prior to the microbial community summary in the spreadsheet was done as outlined in the publication, and results were processed via the DNASense data analysis app (applies Rstudio IDE v.3.5.1 with the ampvis v.2.5.8. package). This version includes rarefaction curves and values of alpha-diversity, richness and evenness per sample in the OTU_table tab. Analytical

The Marsh Ecology Research Program (MERP) was a long-term interdisciplinary study on the ecology of prairie wetlands. A scientific team from a variety of disciplines (hydrology, plant ecology, invertebrate ecology, vertebrate ecology, nutrient dynamics, marsh management) was assembled to design and oversee a long-term experiment on the effects of water-level manipulation on northern prairie wetlands. Ten years of fieldwork (1980 -1989), combines a routine long-term monitoring program and a series of short-term studies, generated a wealth of new and diverse information on the ecology and function of prairie wetlands (Murkin, Batt, Caldwell, Kadlec and van der Valk, 2000). This data set includes belowground macrophyte production data, collected as part of the vegetation section of MERP. Determination of aquatic macrophyte annual net primary production is vital to the understanding of the dynamics of freshwater marshes. Macrophyte biomass, both live and dead, is a major storage compartment for carbon, nitrogen and phosphorus in a marsh and a major potential energy and nutrient source for the faunal component of the marsh ecosystem. Macrophyte communities are also essential structural components of the habitat of both invertebrates and vertebrates. The major objective of the long-term monitoring of aquatic macrophytes was to determine the impact of the wet-dry cycle on macrophyte above and belowground net annual production. Standard harvest techniques were used because they were the most direct, simple and reliable techniques available for estimating net annual primary production of macrophytes per unit area (van der Valk, 1989). In order to estimate net annual belowground macrophyte production, core samples of the belowground biomass were harvested in the late spring and in the fall. Shoot initiation early in the growing season depletes most of the belowground standing crop, and therefore spring sampling was done quickly (within 2 weeks) to capture this state. Underground biomass then reaches its seasonal maxima in the fall and was captured with the fall sampling. The resulting differences between the fall and spring standing crop biomass provided an estimate of net belowground macrophyte production (van der Valk, 1989). References: Murkin, H.R., B.D.J. Batt, P.J. Caldwell, J.A. Kadlec and A.G. van der Valk. 2000a. Introduction to the Marsh Ecology Research Program. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds) H.R. Murkin, A.G. van der Valk and W.R. Clark. pp. 3-15. Ames: Iowa State University Press. van der Valk, A. 1989. Macrophyte production. In Marsh Ecology Research Program: Long-term Monitoring Procedures Manual. (Eds.) E.J. Murkin and H.R. Murkin, pp. 23-29. Manitoba, Canada: Delta Waterfowl Research Station.

Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).