Find research datasets worth reusing

Plastic mulch film residues in agriculture: impact on soil suppressiveness, plant growth, and microbial communities

Plastic mulch film residues have been accumulating in agricultural soils for decades, but so far, little is known about its consequences on soil microbial communities and functions. Here, we tested the effects of plastic residues of low-density polyethylene and biodegradable mulch films on soil suppressiveness and microbial community composition. We investigated how plastic residues in a Fusarium culmorum suppressive soil affect the level of disease suppressiveness, plant biomass, nutrient status, and microbial communities in rhizosphere using a controlled pot experiment. The addition of 1% plastic residues to the suppressive soil did not affect the level of suppression and the disease symptoms index. However, we did find that plant biomasses decreased, and that plant nutrient status changed in the presence of plastic residues. No significant changes in bacterial and fungal rhizosphere communities were observed. Nonetheless, bacterial and fungal communities closely attached to the plastisphere were very different from the rhizosphere communities with overrepresentation of potential plant pathogens. The plastisphere revealed a high abundance of specific bacterial phyla (Actinobacteria, Bacteroidetes, and Proteobacteria) and fungal genera (Rhizoctonia and Arthrobotrys). Our work revealed new insights and raises emerging questions for further studies on the impact of microplastics on the agroecosystems.

opencc-by-4.0May 2022View details →

Data from: Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus

Household Survey Data for journal article 'Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus', without protected data.

opencc-by-4.0Feb 2022View details →

An analytical framework to measure social return of community-supported agriculture

Figures of the case studies (original data from CSA websites and staff interviews)

Soil microbial diversity and community composition during conversion from conventional to organic agriculture

It is generally assumed that the dependence of conventional agriculture on artificial fertilizers and pesticides strongly impacts the environment, while organic agriculture relying more on microbial functioning may mitigate these impacts. However, it is not well known how microbial diversity and community composition change in conventionally managed farmers' fields that are converted to organic management. Here, we sequenced bacterial and fungal communities of 34 organic fields on sand and marine clay soils in a time series (chronosequence) covering 25 years of conversion. Nearby conventional fields were used as references. We found that community composition of bacteria and fungi differed between organic and conventionally managed fields. In the organic fields, fungal diversity increased with time since conversion. However, this effect disappeared when the conventional paired fields were included. There was a relationship between pH and soil organic matter content and the diversity and community composition of bacteria and fungi. In marine clay soils, when time since organic management increased, fungal communities in organic fields became more dissimilar to those in conventional fields. We conclude that conversion to organic management in these Dutch farmers' fields did not increase microbial community diversity. Instead, we observed that in organic fields in marine clay when time since conversion increased soil fungal community composition became progressively dissimilar from that in conventional fields. Our results also showed that the paired sampling approach of organic and conventional fields was essential in order to control for environmental variation that was otherwise unaccounted for.

opencc-zeroJun 2022View details →

Data from: Resource supply and organismal dominance are associated with high secondary production in temperate agricultural streams

Agricultural land use affects the environmental and biological characteristics of stream ecosystems through multiple pathways including nutrient and pesticide contamination, riparian clear-cutting, and hydromorphological degradation. These changes in the abiotic environment can have a direct effect on the productivity of macroinvertebrate communities through environmental filtering and via altered resource conditions encompassing a shift from allochthonous to autochthonous primary production and changes in elemental stoichiometry and food quality. Additionally, macroinvertebrate productivity can be affected indirectly via biological mechanisms, such as changes in species interactions, richness, competition, and predation. We studied the effects of agriculture on structural and functional descriptors of macroinvertebrate communities by assessing environmental characteristics and macroinvertebrate secondary production (MSP), biomass, and density in two forested and two agricultural streams and investigated underlying biotic mechanisms. On average, MSP was 1.6–3.6, biomass 2.8–6.2, and density 5–13 times higher in agricultural than in forested streams. This pattern was associated with higher nutrient concentrations, standing crops of riparian herbaceous vegetation, suspended particulate organic carbon, quantity and quality of epilithic biofilms, and chlorophyll-a concentrations in seston and biofilm of the agricultural streams. Species richness and evenness were significantly lower in agricultural than in forested streams. A negative relationship between MSP and species richness and evenness indicated that density compensation and trait dominance were the prevalent mechanisms facilitating higher MSP in agricultural streams. Our findings suggest that the loss of riparian canopy and excess nutrient conditions are the major environmental drivers contributing to homogenization of ecological niches and dominance of highly productive non-insect generalist species. This study highlights the importance of an ecosystem approach to understanding how complex aggregate stressors affect the regulation of consumer-resource interactions. There is an urgent need to preserve or restore natural riparian vegetation, fostering habitat and resource diversity and limiting nutrient contamination to stream ecosystems.

Innovafrica project data on agricultural food value chains

A dataset was generated using the baseline survey data of the project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - Innovafrica)", which involved 16 istitutions from Europe and Africa and was implemented in six countries namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania. It includes the following information: smallholders' socio-demographic and economic characteristics; improved agriculture practices and seed systems adopted; climate change related aspects; membership in agricultural associations, and access to subsidies, agricultural inputs and credit.

opencc-zeroJul 2022View details →

Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change

Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. Here we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming.

opencc-zeroJul 2022View details →

Different types of semi-natural habitat are required to sustain diverse wild bee communities across agricultural landscapes

1. Semi-natural habitats provide important resources for wild bees in agricultural landscapes. Landscapes under management are dynamic and floral resources fluctuate in space and time. Thus, promoting different semi-natural habitat types within landscapes could be key to support diverse bee meta-communities throughout the season. 2. Here, we integrate analyses of a-diversity (species richness) and b-diversity and species-habitat networks to examine the relative contribution of all major semi-natural habitats to wild bee meta-communities in agricultural landscapes. We sampled extensively and conventionally managed meadows, flower strips, hedgerows and forest edges in spring, early and late summer in 25 landscapes in Switzerland. 3. Habitat types varied in their importance for wild bees throughout the season: While extensively managed meadows supported more rare species, habitat specialists and bee species overall than the other habitat types, flower strips were most important later in the season. Each of the five investigated habitat types harboured relatively unique sets of species with different habitats generally acting as distinct modules in the overall bee-habitat network. 4. Not only flower richness in a habitat per se, but also flower-habitat network properties (habitat strength and functional complementarity) were good predictors of wild bee richness. In addition to local floral richness, landscape composition and configuration interactively influenced b-diversity patterns across habitats. 5. Synthesis and applications. Our study highlights the value of pollinator-habitat network analysis to inform pollinator conservation management at the landscape scale, especially when combined with information on floral resources and flower-habitat networks. Maintaining different types of semi-natural habitats offers diverse and complementary resources throughout the season, which are crucial to sustain diverse wild bee meta-communities in agricultural landscapes. Particularly meadow extensification schemes can play a key role in safeguarding rare and specialist species in these landscapes. While locally a high flower richness promoted bee abundance and richness in general, our results indicate that increasing connectivity between habitat patches in landscapes dominated by arable crops appears to improve species exchange between local bee communities of different habitats, thereby possibly increasing their resilience to disturbances.

opencc-by-4.0Jul 2022View details →

Content Analysis on System Dynamics Modelling Application in Agriculture

The data is a compilation of journal articles retrieved from three databases - Scopus, Web of Science, and Science Direct, using this Boolean search string: ("system dynamics" OR "systems thinking" OR "causal loop diagram") AND ("Agri*" OR "Food" OR "Crop" OR "Meat" OR "Animal" OR "Livestock")).

opencc-zeroJul 2022View details →

Using dietary metabarcoding analyses to characterise waterbirds-agriculture interactions

Globally, the use of agricultural fields by waterbirds has increased, resulting in conflicts with farmers. Designing effective management strategies to resolve these conflicts requires understanding the species' resource use. Dietary analyses can shed light on the extent of consumption of agricultural crops and surrounding natural resources, as well as the potential relationship between diet and an individual's body condition and ultimately its fitness. We examined the dietary composition of the tropical magpie goose (Anseranas semipalmata), seasonally utilising a mixed natural-agricultural landscape of northern Australia. We used DNA metabarcoding of intestinal contents from hunted geese to reconstruct individual diets and evaluated body condition from morphometric measurements. We compared the relative contribution of agricultural and natural foods to dietary composition, and investigated how this contribution varied spatially, temporally, and among individuals that differed in body condition. We found that geese consumed both agricultural and naturally occuring plants assigned to at least 35 taxa. The most frequent and abundant taxa belonged to three families: Poaceae (grasses), Cyperaceae (sedges), and Anacardiaceae (mangoes). Dietary composition varied substantially among sampling sites and over time but not with body condition of geese. Synthesis and applications. We used a novel approach to investigate the diet of a waterbird perceived as problematic across an agricultural landscape in tropical Australia. We showed that individuals forage opportunistically, and that agricultural crops, while eaten, may not represent an essential part of geese diet across the study region. The knowledge acquired provides new insights into the species' foraging ecology offering clear alternatives for mitigating goose-agriculture interactions. Providing disturbance-free alternative foraging areas or minimising the attractiveness of targeted agricultural fields (e.g., shorter grass, alternative ground cover) may alleviate crop consumption while benefiting the species' long-term conservation. While also highlighting the limitations of DNA metabarcoding, our dietary study emphasises the potential of this methodology to improve our understanding of crop damage by wildlife, allowing effective evaluation of management requirements.

Sod translocation to restore habitats of the myrmecophilous butterfly Phengaris (Maculinea) teleius on former agricultural fields

In Europe, 50-70% of former natural grassland area has been destroyed during the past 30 years due to land use changes, losses are expected to increase in the future. Restoration is thought to reverse this situation by creating suitable abiotic conditions. In this paper, we investigate the effects of sod translocation with specific vegetation to facilitate the restoration of a former intensive agricultural field into a wet meadow. First, starting conditions were optimized including modification of the local hydrology, removal of the fertilized topsoil, application of liming, and translocation of fresh clippings as a seed source. The second part aimed at restoring the habitat for the butterfly species Phengaris (Maculinea) teleius, one of the species that was especially affected by the loss of wet meadows. This species engages in a complex myrmecophilous relationship with one host plant, Sanguisorba officinalis, and one obligate host ant, Myrmica scabrinodis. We used sod translocation to create islands of habitat to promote host plant and host ant colonization. After four years following the restoration, we observed that plants spread from the transplanted sods to the surroundings. The vegetation composition and structure of the transplanted sods attracted colonization of Myrmica ants into the restored areas. Following the increase in vegetation cover and height, Myrmica ant colonies further spread into the restored areas. Therefore, sod translocations can be considered an effective restoration method following topsoil removal in the process of restoring wet meadows to provide a starting point for ant colonization and plant dispersion. With these findings, this paper contributes to the evidence-based restoration of wet meadows on former agricultural fields, including complex interactions between invertebrates and their required ecological relationships.

opencc-by-4.0Sep 2022View details →

Urine-enriched biochar: coupling sustainability in sanitation and agriculture

Nitrogen adsorption and plant uptake data and growth data from an urine-enriched biochar experiment.

opencc-by-4.0Sep 2022View details →

Database of Rural Technological Trajectories, their variants and territories featured by peasantries of the Brazilian Northern Region based on Agricultural Censuses and special tabulations for the Economy of Agroforestry Systems (2006 and 2017)

Introduction This database contains the string variables at municipal level that qualifies the techno-productive trajectories (TT) of the Brazilian Northern Region Agrarian Economy, their technological variants (TTV) and territories based on peasantries. The TTs and TTVs were defined and theoretically justified by Costa (2021, p. 217-219). Delimitation of Technological Trajectories, their variants and territories featured by peasantries The TTs are designed by a method that combines differentiation and structural signification of rural production in each territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU). Structural differentiation (Phase 1) is necessary because production systems activities play different roles, depending on the systems  production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 440 and 441 of Costa (2021). In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs structures that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The codes of variable “Technological Trajectories” in this database are “CamponêsT1” for “Peasant Trajectory.T1” in Costa, 2021; “CamponêsT2” for “Peasant Trajectory.T2”; “CamponêsT3” for “Peasant Trajectory.T3”; “PatronalT4” for “Employer.T4”; “PatronalT5” for “Employer.T5”; “PatronalT7” for “Employer.T7”. In turn, the procedures to get the technological variants of TTs (TTVs) for census years 2006 and 2017 are described in Costa, 2021, p. 447-451. The codes of variable “Technological Variants” in this database are “IQ” for “CI = Chemical Intensity” in Costa, 2021; “IM” for “MI = Mechanical Intensity”; “IT” for “LI = Labour Intensity”; “IPst” for “PI = Pasture Improvement”; “IReb” for “HI = Herd improvement”; “Crg” for “LoadC=Load Capacity of Pasture”; “SAF-F” for “AFSs-F = AFSs with the presence of forest management”; “SAF-A” for “AFSs-A = Artificially developed AFSs”; “+” after the attribute for “Attribute clearly verified; “–“ for “Attribute clearly absent”; “0” for “an uncertain attribute”. The Brazilian Northern Region encompasses the municipalities of the federative states Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins. A municipality is codified by the variable “Peasantry” as “ACaboclo_Originário”, meaning a territory of an “original caboclo peasantry (OcP in English or CbO in Portuguese)”, if founded before 1880; “BCaboclo_Forâneo”, meaning a territory of an ”immigrant caboclo peasants (IcP or CbF)”, if founded between 1880 and 1910; “CAgrícola_Forâneo, meaning a territory of a “post-ruber immigrant agricultural peasantry (IpR or FpB)”, if founded  between 1910 and 1960; and “DContemporâneo”, meaning a “recent peasantry (ReP or ReC)”, if founded since 1960. The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 2006 and 2017 Agricultural Censuses. The following special cases were handled: <ul> <li>In the Agricultural Census 2017 credit data were not available. However, the Central Bank of Brazil informs for that year total rural credit for family-based and non-family-based agriculture and livestock by municipality.</li> <li>Comparing the production of manioc in the 2006 census with the production of manioc flour in the same year and with the historical production, we arrived at errors in three municipalities in Pará: in Moju a manioc production of 498,907 t is recorded adding the two sets of data (Peasant and Employer), when in fact it is 42,132; in São Miguel do Guamá the figure of 392,784 t is recorded when it actually is 175,941; 204,216 is recorded in Viseu and 106,287 is actual figure. Corrections were made using the proportion manioc/manioc flour prevailing in other municipalities in same microregion.</li> <li>In the census there are the value of the production of manioc and the value of the production of manioc flour. Since we are dealing with the same producer, if we consider in gross value of production or income aggregations both products, we incur double counting. In such cases, the value related to manioc flour was considered.</li> <li>The 2006 census has information on fishing restricted to the monetary income from the sale of fish, as a complementary income variable. The information does not incorporate the value of fish consumed in the establishment. Therefore, it is not a variable equivalent to the GVP of all other products considered. In turn, the 2017 census provides data on fish production as part of livestock (the gross value of fish production in captivity, which makes up the gross value of livestock production) but does not maintain the fish sales variable from the previous census. This income is contained in the variable “other producer income”, in which, none of the other possibilities listed (esgargot, etc.) are adhered to T2 (IBGE, Censo Agropecuário de 2017. Rio de Janeiro, IBGE, 2018). Therefore, two things were done to incorporate fisheries: a) for 2006 the GVP of fishery production was considered the variable "fish sale" under the heading "other producer income" divided by 1 minus the self-consumption rate 31% (Costa et al, 2022); b) for 2017, the GVP of fisheries production resulted from the division of the variable “other producer's incomes” by the same denominator of the operation described in “a”.</li> </ul> The dataset is organized as: 1. Data set with variables delimiting TT, TTV and Peasantry 2006_NorthRegion_TechVariants.csv 2017_ NorthRegion_TechVariants.csv. In each table the column names are self-explanatory. 2. Dataset with special tabulation or the agroforestry systems economy represented by Peasant Trajectory.T2     Gross Value of Production     Table1_NorthRegion_T2_GVP.csv      Table 1a – Gross Value of Productios (GVP) by products of AFSs-F and peasantry 2006 and 2017     Table 1b – Gross Value of Productios (GVP) by products of AFSs-A and peasantry, 2006 and 201     Table 1c – Gross Value of Productios (GVP) by products of T2, technological variant, and peasantry, 2006 and 2017       Real Product      Real Product” (RP): For each year (i), the vector of produced quantities (Qi) multiplied by a vector of fixed prices (P1): variation of RP is explained exclusively by the variation of Q.        Table2_NorthRegion_T2_RealProduct.csv       Table 2a – T2 Production by technological variant and peasantry, 2006       Table 2b – T2 Production Value (=Real Product) by technological variant, and peasantry, 2006 in R$ 1,000 currents       Table 2c – T2 Implicit prices by technological variant, and peasantry, 2006, R$ 1.000 currents (each cel in Table 2b divided by corresponding cel in Table 2a)       Table 2d – T2 Production by technological variant, and peasantry, 2017       Table 2e – T2 Real Product1 by technological variant, and peasantry, 2017 in R$ 1,000 from 2006 (each cel in Table 2c multiplied by corresponding cel in Table 2d)        Key variables       Table3_NorthRegion_T2_KeyVariables.csv        Table 3 – Key variables of T2 economy by peasantry, 2006 and 2017 Reference: Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2). COSTA, F. A., FEIJÃO,, L. G., ALMEIDA, I. C., NOGUEIRA, K. N. S., AMERICO, M. C. (2022). Database of a Riverine Economy in Mocajuba, Low Tocantins, Pará, Amazonia, Brazil [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121336

opencc-by-nc-4.0Oct 2021View details →

Figure 5 in Agricultural activities and threat to fauna in Brazil: an analysis of the Red Book of Endangered Brazilian Fauna

Figure 5. Number of species affected by isolation, in each group, compared to the number of species affected by habitat reduction and habitat fragmentation.

opencc-by-4.0Oct 2022View details →

Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application

This data set contains data related to a Mowing Intelligent Tool (MowIT). Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution. The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT. Two different data collections were performed in different open-air environments with grass to cut. In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7. Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used. In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass.  In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.

opencc-zeroOct 2022View details →

Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis

To improve our understanding of clearing natural ecosystems for cropland on soil organic carbon stocks in drylands, we searched for related peer-reviewed research papers published from 1980 to 2022 on the Web of Science (<a href="https://www.webofscience.com">https://www.webofscience.com</a>) and the Scopus Database (<a href="https://www.scopus.com">https://www.scopus.com</a>) (accessed on 30th April 2022). Then, we screened papers for integrity, relevance, and scientific merit under the following criteria: (1) We made sure all studies were independent and based on field-measured data; (2) Each study had to report paired SOC stocks of cropland and adjacent natural ecosystems with the same or a similar suite of environmental factors; (3) Studies need to explicitly present results on SOC stocks or concentrations for certain depths and areas; (4) Studies have specified the types of natural ecosystems that were converted to cropland, which are used as criteria for defining CNEC types. Finally, we winnowed results to a total of 159 scientific journal articles, comprising 242 sites with 1379 paired soil layer observations from 601 paired soil profiles.

opencc-zeroDec 2023View details →

Data from: Movement of avian predators points to biodiversity hotspots in agricultural landscape

Global agricultural landscapes are witnessing a concerning decline in biodiversity, and this trend is predicted to persist. To safeguard these biodiversity-rich areas, it's crucial to pinpoint hotspots effectively. In doing so, we utilized various species of birds of prey as suitable sentinel animals due to their mobility and dependence on prey diversity and abundance. Between 2019 and 2021, we tracked 62 individuals from four predator species using GPS loggers in Estonian farmland. Dividing the study area into 50-meter grids and overlaying them with tracked individuals' locations enabled us to differentiate between hotspots of their activity and control sites. We conducted surveys on amphibians, birds, small mammals, and plant diversity to determine if avian predator activity hotspots correlated with overall biodiversity. Our findings revealed significantly higher diversity and abundance in the surveyed groups within activity hotspots compared to control sites. These hotspots continued to be frequented by raptors in the subsequent year, albeit not two years later. In conclusion, multispecies GPS telemetry of avian predators emerges as an objective, dependable, and spatially accurate biodiversity indicator. With the accumulation of movement data, we anticipate increased interest and adoption of this approach in biodiversity monitoring.

embargoedcc-by-4.0Apr 2024View details →

Agricultural plastic waste in Italy-Spain-Greece-Portugal

Agricultural plastic waste in South Europe (Italy, Spain, Greece and Portugal) at the NUTS 2 regional level (EUROSTAT).

opencc-by-4.0Apr 2024View details →

Raw data for the manuscript: Multigenerational toxicity of microplastics derived from two types of agricultural mulching films to Folsomia candida

Survival and reproduction data from multigenerational single species tests involving two types of plastic materials.