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opencc-by-4.0Nov 2021View details →

Large-eddy simulation of airborne wind energy farms: AWES virtual flight data

Large-eddy simulation of airborne wind energy farms: AWES virtual flight data. This dataset contains virtual flight data collected from individual systems in airborne wind energy parks obtained by means of large-eddy simulations. All data are stored as Python dictionary objects in the Pickle format. Additional Python scripts are provided to visualize the data.

opennotspecifiedAug 2011View details →

Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest. in Suidae

Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest.

opencc-zeroDec 2021View details →

Data from: Introgression from farmed escapees affects the full life cycle of wild Atlantic salmon

After a half a century of salmon farming, we have yet to understand how the influx of genes from farmed escapees affects the full life history of Atlantic salmon (Salmo salar L.) in the wild. Using scale samples of over 6900 wild adult salmon from 105 rivers, we document that increased farmed genetic ancestry is associated with increased growth throughout life and a younger age at both seaward migration and sexual maturity. There was large among-population variation in the effects of introgression. Most saliently, the increased growth at sea following introgression declined with the population's average growth potential. Variation at two major-effect loci previously shown to be associated with age at maturity was little affected by farmed genetic ancestry and could not explain the observed phenotypic effects of introgression. Our study provides knowledge crucial for redicting the ecological and evolutionary consequences of increased aquaculture production worldwide.

opencc-zeroJan 2022View details →

Data from: Turnover and nestedness drive plant diversity benefits of organic farming from local to landscape scales

Biodiversity-benefits of organic farming have mostly been documented at the field scale. However, these benefits from organic farming to species diversity may not propagate to larger scales, because variation in the management of different crop types and semi-natural habitats in conventional farms might allow species to cope with intensive crop management. We studied flowering plant communities using a spatially replicated design in different habitats (cereal, ley and semi-natural grasslands) in organic and conventional farms, distributed along a gradient in proportion of semi-natural grasslands. We developed a novel method to compare the rates of species turnover within and between habitats, and between the total species pools in the two farming systems. We found that the intra-habitat species turnover did not differ between organic and conventional farms, but that organic farms had a significantly higher inter-habitat turnover of flowering plant species compared to conventional ones. This was mainly driven by herbicide-sensitive species in cereal fields in organic farms, as these contained 2.5 times more species exclusive to cereal fields compared to conventional farms. The farm-scale species richness of flowering plants was higher in organic than conventional farms, but only in simple landscapes. At the inter-farm level, we found that 36% of species were shared between the two farming systems, 37% were specific to organic farms while 27% were specific to conventional ones. Thus, our results suggest that that both community nestedness and species turnover drive changes in species composition between the two farming systems. These large-scale shifts in species composition were driven by both species-specific herbicide and nitrogen sensitivity of plants. Our study demonstrates that organic farming should foster a diversity of flowering plant species from local to landscape scales, by promoting unique sets of arable-adapted species that are scarce in conventional systems. In terms of biodiversity conservation, our results call for promoting organic farming over large spatial extents, especially in simple landscapes, where such transitions would benefit plant diversity most.

opencc-zeroFeb 2022View details →

Data from: Rich resource environment of fish farms facilitates phenotypic variation and virulence in an opportunistic fish pathogen

Phenotypic variation is suggested to facilitate the persistence of environmentally growing pathogens under environmental change. Here we hypothesized that the intensive farming environment induces higher phenotypic variation in microbial pathogens than natural environment, because of high stochasticity for growth and stronger survival selection compared to the natural environment. We tested the hypothesis with an opportunistic fish pathogen Flavobacterium columnare isolated either from fish farms or from natural waters. We measured growth parameters of two morphotypes from all isolates in different resource concentrations and two temperatures relevant for the occurrence of disease epidemics at farms and tested their virulence using a zebrafish (Danio rerio) infection model. According to our hypothesis, isolates originating from the fish farms had higher phenotypic variation in growth between the morphotypes than the isolates from natural waters. The difference was more pronounced in higher resource concentrations and the higher temperature, suggesting that phenotypic variation is driven by the exploitation of increased outside-host resources at farms. Phenotypic variation of virulence was not observed based on isolate origin but only based on morphotype. However, when in contact with the larger fish, the less virulent morphotype of some of the isolates also had high virulence. As the less virulent morphotype also had higher growth rate in outside-host resources, the results suggest that both morphotypes can contribute to F. columnare epidemics at fish farms, especially with current prospects of warming temperatures. Our results suggest that higher phenotypic variation per se does not lead to higher virulence, but that environmental conditions at fish farms could select isolates with high phenotypic variation in bacterial population and hence affect evolution in F. columnare at fish farms. Our results highlight the multifaceted effects of human-induced environmental alterations in shaping epidemiology and evolution in microbial pathogens.

opencc-zeroDec 2021View details →

Changes in arthropod communities mediate the effects of landscape composition and farm management on pest control ecosystem services in organically managed strawberry crops

Landscape composition and local diversification practices such as polyculture, cover cropping, and hedgerows may promote natural pest control by benefiting natural enemy communities on farms. Our study employs piecewise structural equation modeling (PSEM) to test causal hypotheses regarding the effects of landscape composition and local diversification practices on arthropod communities and pest control ecosystem services. We sampled 27 organic strawberry fields in California's Central Coast region in 2015 and 2016 (17 repeated between years) for a total of 37 distinct sites across years. The sites were selected along orthogonal gradients of landscape composition and local diversification practices. We also investigated the effects of two common pest management practices. At each site, we sampled arthropod communities using a hand-held vacuum and performed sentinel prey experiments using the pest species Lygus hesperus to estimate pest control levels. At the landscape scale, proportion woods increased natural enemy abundance; at the local scale, diversification practices increased natural enemy diversity. Insecticides and tractor vacuuming, aimed at controlling pests, were indirectly detrimental to pest control services. Both practices decreased natural enemy abundance, and while insecticides also decreased pest abundance, vacuuming did not. Natural enemy abundance and diversity increased pest control levels, while pest abundance had the opposite effect. The PSEM results confirmed our hypotheses that landscape and local effects on pest control are mediated through changes in arthropod communities. <a>Synthesis </a>and applications: At the landscape scale, higher proportions of woody habitat are associated with greater natural enemy abundance, which increases pest control levels. When promoting pest control ecosystem services is a policy goal, regional planners should prioritize the conservation and restoration of woodlands in agricultural landscapes. At the local scale, decisions of individual growers can impact pest control services. For many growers, adopting practices that promote on-farm plant diversity may be a feasible solution for increasing pest control levels while avoiding the environmental and economic costs imposed by insecticide application and tractor <a>vacuuming. </a> <div> <div> <div class="msocomtxt"> </div> </div> </div>

openMar 2022View details →

Raw data for "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" (2022)

This dataset accompanies the publication "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" by Stratton et al. in the journal Agriculture, Ecosystems, and Environment (2022). <a href="https://doi.org/10.1016/j.agee.2022.107925">https://doi.org/10.1016/j.agee.2022.107925</a> METHODS: We conducted our experiment between May 2018 and December 2019 on 14 farms in the eastern coastal highlands region of Santa Catarina, Brazil. The mean altitude of sites was 467 m (+/- 161 m).  Eastern Santa Catarina has a subtropical climatic pattern, with mean annual rainfall ranging from 1,500-1,700 mm (Wrege et al., 2012). While 2018 had typical weather patterns for the region, 2019 was a dry year, particularly during the spring months (Appendix B, Table B.1). All farms were located in the Colonial Serrana Catarinense soil microregion, one of 16 designated microregions in the state of Santa Catarina (EMBRAPA, 2004). Primary soil types in our study site are associations of dystric Cambisols and haplic Acrisols (typic Dystrocryepts and typic Paleudults in the USDA Soil Taxonomy), which tend to be moderately to highly acidic, with limited soil nutrient availability and moisture retention (EMBRAPA, 2004; IUSS Working Group WRB, 2015; USDA, 2010). To support crop production, farmers in the region typically apply lime (calcium and magnesium carbonate) to agricultural fields to increase soil pH from <5.5 to 6 (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). Exact farm locations within the region are not given and farmer identities have been anonymized. Experimental design The fully factorial experiment had six treatments (Figure 2): (1) cover crop + pea-cucumber intercrop, (2) cover crop + pea monocrop, (3) cover crop + cucumber monocrop, (4) fallow + pea-cucumber intercrop, (5) fallow + pea monocrop, and (6) fallow + cucumber monocrop. Due to the timing of farm recruitment, only conventional and transitioning farms participated in the first year of cover cropping (2018); agroecological farms were added to the study during the vegetable intercropping period of 2018 and had their first round of cover cropping in 2019. The cover crop mixture treatment was designed to emulate traditional practices in the region, as well as to include functionally complementary legume and grass species: common vetch (Vicia sativa L.) and black oat (Avena strigosa Schreb). We also selected vegetables with distinct ecological functional traits, such that intercropping represented an increase in functional diversity relative to mono-cropped vegetables. Snow peas are N-fixing legumes with a vining, upright structure and a deep root system, whereas cucumbers are low-lying, non-legume cucurbits that provide groundcover and have a relatively shallow, extensive root system.   Cover crop treatments consisted of two adjacent 50 m2 plots in each field, one of which was planted with the cover crop mixture; the other served as a weedy fallow control. In 2018, the cover crop mixture seeding rate was 72 kg/ha black oat and 36 kg/ha common vetch. Due to poor vetch performance in mixtures at this rate, we increased the vetch seeding rate to 60 kg/ha in 2019, maintaining the black oat rate from 2018. Cover crop seeds were inoculated with the Brazilian strain Rhizobium etli (SEMIA 384; source: FEPAGRO) at 4 g/kg vetch seed prior to planting. Cover crops were grown until peak flowering, and then cover crops (and weeds in the fallow) were incorporated into the soil by rototiller (n = 7 farms) or by hand hoeing (n = 7 farms), based on farms’ available machinery, between September 5-10 in 2018 and September 10-18 in 2019 (approximately one week following cover crop sampling on each farm).   Vegetables were planted two weeks following cover crop and weed biomass incorporation within a period of 7-10 days across sites. Harvest dates were spaced such that crops were growing for approximately the same period across farms. The 50 m2 plots were each divided into three intercrop treatments with a ~1 m2 pathway between each treatment, for a total of 6 treatments randomly assigned to plots per 100 m2. We planted a climbing variety of snow peas (Pisum sativum subsp. sativum var. macrocarpum, “Torta de flor roxa”) and pickling cucumber (Cucumis sativa L. var. Pepino HT 05) in intercrops and in their respective monocrops, using a replacement design (i.e., equivalent crop densities in all treatments). Snow pea seeds were inoculated with Rhizobium leguminosarum var. viceae (SEMIA 3007/BR 619, source: UFSC ENR/CCA) at a rate of 4 g/kg directly prior to planting. There were five rows of crops per treatment, with only the three middle rows harvested to limit edge effects. In-row spacing was 60 cm for cucumber and 20 cm for peas, with 60 cm between rows in both intercrops and monocrops. Cucumbers were grown as starts for 2.5 weeks before planting, and peas were planted from seed on the same planting date as cucumber starts.   In the summer between January and May 2019 all fields were planted to a sunflower (Helianthus annuus L.) crop, which was incorporated into the soil during flowering approximately two weeks prior to cover crop planting in 2019. Because we sought to understand the effects of crop diversification given existing water and nutrient limitations on working farms, the experiment was entirely rainfed and legume N fixation was the sole external N source. Soil sampling and analysis Prior to the first cover cropping period, we collected a composite sample of 15-20 soil cores (2.5 cm diameter, 20 cm depth) on both the cover crop and fallow sides of each experimental field (n = 28) for analysis of baseline conditions (see Appendix B for full details). Briefly, soil was analyzed for pH, macro- and micronutrients, and soil organic matter (SOM) by the Santa Catarina State Agricultural Agency (EPAGRI) in Ituporanga, Santa Catarina, Brazil, using standard protocols (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). pH was measured with a glass electrode both with and without Sikora’s buffer, and buffered pH is used throughout this paper (Tecnal TEC-11 MP). Soil organic C and total soil N to 20 cm were determined by dry combustion on a Leco TruMac CN Analyzer (Leco Corporation, St. Joseph, Michigan, USA). We measured soil texture (% clay, sand, and silt) using a total dispersion method with sodium hexametaphosphate (Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), 1997). Bulk density was estimated from the mass of 10 fresh soil cores per treatment, with subsequent accounting for soil moisture.   We measured C mineralization as a baseline indicator of soil microbial activity and biological soil fertility at the start of the experiment, and N mineralization as a response variable following the second year of cover crop treatments. Specifically, using the baseline soil sample, we conducted a short-term (24-hour) C mineralization assay to determine potentially mineralizable C (PMC), which measures the flux of CO2 following re-wetting of previously air-dried, sieved soil using a Li-Cor (Franzluebbers et al., 2000; Hurisso et al., 2016). To measure potentially mineralizable N (PMN), we conducted a two-week aerobic incubation using fresh soil collected at vegetable crop planting in the second year of the experiment (spring 2019), two weeks after cover crop and weed biomass incorporation (Drinkwater et al., 1996; Appendix B.2). PMN was calculated as the difference between extractable soil inorganic N (NH4+ and NO3-) at the start and end of the incubation. We used pre-incubation extractable inorganic N concentration (mg/kg) as a measure of soil inorganic N availability at vegetable crop planting.  Cover crop sampling and analysis Cover crop biomass sampling took place from August 28-September 2 in 2018 and September 4-10 in 2019. During peak flowering of both common vetch and black oat, we destructively harvested the aboveground biomass of cover crop mixtures and weedy fallows from two 0.5 x 0.5 m quadrats of each treatment per field. We took care to avoid treatment edges, cut plant material to the soil surface, and separated harvested plant material by species, grouping all weeds together. Aboveground biomass was dried in a forced-air oven at 60 °C for 48 hours. Following grinding in a Wiley mill to 2 mm, % N and C content was determined by dry combustion on an elemental analyzer (Leco, as above). Community-weighted means were calculated for total aboveground biomass C and N in cover crop species and weeds, to determine the overall C and N inputs to soil following incorporation of biomass on each farm. We measured biological N2 fixation in inoculated common vetch from the cover crop phase of the experiment in 2018 and 2019. Vetch N fixation was estimated using the 15N natural abundance method (Shearer and Kohl, 1986), which compares stable N isotope ratios in the legume and reference species (oat monocultures) (Appendix C).  Vegetable crop sampling and analysis To capture the full production period of both cucumber and pea crops, yield was measured in two harvests, which were approximately 14 days apart on each farm. Harvest dates ran from November 16-December 6 in 2018 and November 20-December 4 in 2019. We measured yield by weighing all harvestable fruit from three designated, representative row sections (6 plants on average per row) per crop type per treatment. Rows were sampled from the center of each treatment to reduce edge effects. We calculated yield as total crop production (g) per plant harvested in each row. Mean yield for each crop type was calculated as the average of the three harvested rows per treatment on a per-plant basis and was then aggregated to the plot and hectare level based on experimental planting densities. Total N harvested, or “N yield”, was calculated for all treatments by multiplying the % N in each vegetable crop by its yield (kg/ha) after accounting for crop water content. Using plot-level yield data, we subsequently calculated the relative yield total (Land Equivalent Ratio, LER) for intercrop treatments by farm using the standard equation (Vandermeer, 1989) (Table 1). As a relative measure of total crop production per area, when mean LER > 1, intercrops were considered to have “overyielded” compared to their component monocrops. We calculated the LER for N yield (LERN in kg N/ha) using the same formula.   At the second vegetable harvest, we destructively sampled whole aboveground crop biomass, including residues and remaining fruits, from the designated experimental rows.  Following the harvest, a minimum of six representative cucumbers per treatment (from different plants) per farm were washed in deionized water, air-dried, sliced, and the middle sections were combined into a homogenized, composite sample of ~100 g and then dried for one week at 60 ºC. All peas from each treatment’s subplot were washed in deionized water, air-dried, de-stemmed, chopped, and each homogenized sample (35-60 g fresh material) was subsequently dried at 60 ºC in a forced-air oven for 48 h to one week, until fully desiccated. Dried vegetable biomass residues were ground using a Wiley mill; vegetable crop samples were ground in a coffee grinder; and all vegetable samples were analyzed for % C and N on a LECO elemental analyzer. See supplemental material from Stratton et al. 2022 for further detailed information on methods.

openDec 2021View details →

A one-year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME -- Model data

This file contains supporting data for the manuscript "A one-year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME" by van Stratum et al. 2022 in JAMES.  - model_output: contains NetCDF files with HARMONIE-AROME for specific columns for the lidar locations, only for the WIPAFF flight comparison the full 3D hourly fields are given for one day. Files containing "DOWA_40h12tg2_fERA5_ptE" are the reference simulations and files containing "DOWA_40h12tg2_fERA5_WF2019_fix" are output from the wind farm parameterisation simulations.  - input_HARMONIE_WFP: contains the input files used for the wind farm parameterisation, where wind_turbine_coordinates.tab contains the locations of all wind turbines and the turbine type, and wind_turbine_0XX.tab the cp/ct curves, radius and hub height for each turbine type.  The measurements used in the manuscript are from various external sources and should be downloaded separately.

opencc-zeroMay 2022View details →

Personality and predictability in farmed calves using movement and space-use behaviours quantified by Ultra-wideband sensors

Individuals within a population often show consistent between individual differences in their average behavioral expression (personality), and consistent differences in their within-individual variability of behavior around the mean (predictability). Where correlations between different personality traits and/or the predictability of traits exist, these represent behavioral or predictability syndromes. In wild populations, behavioral syndromes have consequences for individual's survival and reproduction and affect the structure and functioning of groups and populations. The consequences of behavioral syndromes for farm animals is less well explored, partly due to the challenges in quantifying behavior of many individuals across time and context in a farm setting. Here, we use Ultra-Wideband location sensors to provide precise measures of movement and space use for 60 calves over 40-48 days. We find that individuals show consistent within and between individual variation in movement and space use and show correlations in personality and predictability, indicating the existence of "exploratory" and "active" personality types in farmed calves. We consider the consequences of such individual variability for cattle behavior and welfare and how such data may be used to inform management decision in farm animals.

opennotspecifiedJul 2018View details →

Distribution. NE South Africa, known only from three small, widely separated locations: Bronberg Ridge area in SE Pretoria, Gauteng Province (18 sites), Nylsvley Nature Reserve and surrounding farms on Nyl floodplain in Modimolle district, Limpopo Province (9 sites), and near Pretoriuskop Camp, Fayi area (Macili and Machulwane), and Numbi Gate in SW Kruger National Park, Mpumalanga Province (4 sites). in Chrysochloridae

Distribution. NE South Africa, known only from three small, widely separated locations: Bronberg Ridge area in SE Pretoria, Gauteng Province (18 sites), Nylsvley Nature Reserve and surrounding farms on Nyl floodplain in Modimolle district, Limpopo Province (9 sites), and near Pretoriuskop Camp, Fayi area (Macili and Machulwane), and Numbi Gate in SW Kruger National Park, Mpumalanga Province (4 sites).

opencc-zeroJun 2022View details →

Microarthropod abundance data of a drought experiment comparing organic and conventional farming

In Central Europe summer droughts are increasing in frequency which threatens production and biodiversity in agroecosystems. The potential of different farming systems to mitigate detrimental drought effects on soil animals is largely unknown. We investigated the effects of simulated drought on the abundance and community composition of soil microarthropods (Collembola, Oribatida, Meso‑, Pro‑ and Astigmata) in winter wheat fields under long-term conventional and organic farming in the DOK trial, Switzerland. We simulated drought by excluding 65% of the ambient precipitation during the wheat growing season from March to June 2017. The abundance of Collembola and Oribatida declined more consistently in conventionally compared to organically managed fields under simulated drought. The abundance of Collembola as well as Meso‑, Pro‑ and Astigmata, but not the abundance of Oribatida, increased in deeper soil layers due to simulated drought, suggesting vertical migration as drought avoidance strategy. The species composition of Oribatida communities, but not of Collembola communities, differed significantly between drought treatments as well as between farming systems. Soil carbon content was a major factor structuring Oribatida communities. Our results suggest that organic farming buffers negative effects of drought on soil microarthropods, presumably due to higher soil carbon content, and associated higher soil moisture and improved soil structure. This potential of organic farming systems to mitigate consequences of future droughts on soil biodiversity is promising and needs further exploration across larger climatic and spatial scales and should be extended to other groups of soil biota.

opencc-by-4.0Jul 2022View details →

Measuring deposition of ammonia emitted by a dairy farm using biomonitors

Raw data for bachelor project where biomonitors were used to measure nitrogen deposition around a dairy farm.

opencc-zeroAug 2022View details →

Semi-intensive Shrimp-Farms as Experimental Arenas for the Study of Predation Risk from Falcons to Shorebirds

Varying environmental conditions and energetic demands can affect habitat use by predators and their prey. Anthropogenic habitats may provide an opportunity to document both predation events and foraging activity by prey, and therefore enable an empirical evaluation how prey cope with trade-offs between starvation and predation risk in environments of variable foraging opportunities and predation danger. Here we use seven years of observational data of peregrine falcons Falco peregrinus and shorebirds at a semi-intensive shrimp-farm to determine how starvation and predation risk vary for shorebirds under a predictable variation in foraging opportunities. Attack rate (mean 0.1 attacks/hr, equating 1 attack every ten hours) was positively associated with the total foraging area available for shorebirds at the shrimp-farm throughout the harvesting period, with tidal amplitude at the adjacent mudflat having a strong non-linear (quadratic) effect. Hunt success (mean 14%) was higher during low tides, and declined as the target flocks became larger. Finally, individual shorebird vigilance behaviours were more frequent when birds foraged in smaller flocks at ponds with poorer conditions. Our results provide empirical evidence of a risk threshold modulated by tidal conditions at the adjacent wetlands, where shorebirds trade-off risk and rewards to decide to avoid or forage at the shrimp-farm (a potentially dangerous habitat) depending on their need to meet daily energy requirements. We propose that semi-intensive shrimp farms serve as ideal 'arenas' for studying predator-prey dynamics of shorebirds and falcons, because harvest operations and regular tidal cycles create a mosaic of foraging patches with predictable food supply. In addition, the relatively low hunt success suggests that indirect effects associated to enhanced starvation risk are important in shorebird life-history decisions. --

opennotspecifiedSep 2022View details →

FIGURE. Petalidium mannheimerae, habitat and habit. A. Plant in flower during a particularly dry season (Aussenkjer [Aussenkehr] 147 Farm, ||Kharas Region, Namibia). B. Plant in full flower (Kosies, Richtersveld, Northern Cape, South Africa). Photographs by L. Nanyeni (A) & M. Koekemoer (B). in Petalidium mannheimerae (Acanthaceae), a new species from Namibia and South Africa, with notes on the taxonomic identity of P. parvifolium

FIGURE. Petalidium mannheimerae, habitat and habit. A. Plant in flower during a particularly dry season (Aussenkjer [Aussenkehr] 147 Farm, ||Kharas Region, Namibia). B. Plant in full flower (Kosies, Richtersveld, Northern Cape, South Africa). Photographs by L. Nanyeni (A) & M. Koekemoer (B).

Fig. 8 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 8. Time-calibrated phylogeny of the tribe Attini inferred from an MCMCTREE analysis (Yang and Rannala 1997). Red star indicates the origin of fungusfarming ants. Blue dots indicate fossil calibrations (see text). Colored boxes indicate the five agricultural systems: (i) lower agriculture (yellow); (ii) coral-fungus (Pterulaceae) agriculture (red); (iii) yeast agriculture (green); (iv) higher agriculture (blue); and (v) leaf-cutter agriculture (orange).Taxa in red correspond to anthead images on right:Cyphomyrmex costatus (top),Paramycetophylax bruchi (middle), and C. rimosus (bottom). Light blue bars indicate 95% highest probability density (HPD). K-Pg Event = Cretaceous/Paleogene boundary; EECO = Early Eocence Climatic Optimum;TEE = Terminal Eocene Event; MMCO = Middle Miocene Climatic Optimum.

Fig. 9 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 9. Phylogeny of fungal cultivars and free-living fungi based on Bayesian analysis of the ITS 'fungal barcoding' region conducted in MrBayes (Ronquist et al. 2012). Ant-cultivated fungi are divided into two clades, Clade 2 (red branches) and Clade 1 (blue branches); free-living fungi are represented by black branches. The cultivar of Paramycetophylax bruchi is represented with a blue font and a red star. Nodes with posterior probability of 1 are represented by a blue circle, whereas nodes with posterior probabilities between 0.90 and 0.99 are represented by an orange circle. Nodes without circles indicate posterior probabilities lower than 0.90. Green box indicates the clade of fungi cultivated by the yeast-growing ants in the genus Cyphomyrmex and the blue box indicates the clade of fungi cultivated by the leaf-cutting ants and Apterostigma megacephala.

Fig. 7 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 7. Phylogeny of the Myrmicinae based on a SWSC-EN partitioned maximum-likelihood analysis in IQTREE (Chernomor et al. 2016, Minh et al. 2020). Support values on branches indicate the SH-like approximation likelihood-ratio test (alrt) and ultrafast bootstrap (UFBoots) proportions, respectively. Red branches indicate the position of Paramycetophylax bruchi. Inset: Automontage image of the head of a P. bruchi worker.

Fig. 6 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 6. Fungus gardens. (A) Subterranean garden chamber at Reserva Biósfera Ñacuñan (RBÑ), Mendoza, Argentina; (B) fungus garden and ants in field nest boxes at Parque Nacional Sierra de las Quijadas, San Luis, Argentina; (C–D) fungus garden and ants in field nest boxes at RBÑ.

Fig. 4 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 4. Nest entrances of Paramycetophylax bruchi at (A) El Borbollón, Mendoza,Argentina; (B) Parque Nacional Sierra de las Quijadas, San Luis, Argentina; and (C) Reserva Biósfera Ñacuñan, Mendoza, Argentina. (D and inset) Nest entrance from Reserva Nacional Pizarro (Salta) of Kalathomyrmex emeryi shown here for comparison.

Fig. 5 in The Last Piece of the Puzzle? Phylogenetic Position and Natural History of the Monotypic Fungus-Farming Ant Genus Paramycetophylax (Formicidae: Attini)

Fig. 5. Illustration of Paramycetophylax bruchi nests at (A) Parque Nacional Sierra de las Quijadas, San Luis, Argentina; and (B) Reserva Biósfera Ñacuñan, Mendoza, Argentina.