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86 results for “organic management”
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
The relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities
<p>Descripition of the dataset:</p> <p>The questionnaire was made to the leaders of the companies was made in July 2019, consisting of 10 questions where the subject under study is addressed. <br> The questionnaire was personally conducted to 390 leaders of Madrid, consisting of 10 questions ranging from demographic variables, leadership style in conflict management and methods used for conflict resolution.<br> The database has been subsequently anonymised, so that each respondent cannot be identified.</p> <p>Dataset relations:</p> <p>Paper: What are the determinants in conflict management and leadership employed? An analysis of the determinants for the Spanish case</p> <p>Abstract:The purpose of this study is to examine the relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities. <br> For this reason, an exhaustive review of the literature on leadership decision-making and conflict management has been carried out, focusing on sociodemographic variables that have the greatest influence. <br> In addition, a study of conflict management has been carried out in the community of Madrid. The purpose of the study was to identify the most decisive sociodemographic variables that explain how leaders manage conflicts in the most important business nucleus in Spain. <br> The statistical study was based on information collected on demographic variables and leadership styles in conflict management through a survey carried out in July 2019 of 394 leaders of Madrid organizations.</p>
Figure 7 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 7. Annual, perennial, dicot, and monocot weed biomass in each weed management treatment pooled across fields. Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 6 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 6. Diversity indices of weed communities for all treatments. Weed by species biomass was pooled across fields.Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05).Error bars are standard errors and treatments are abbreviated:NC,nontreated control; SR,seeding rate; IM, interrow mower;WZ,Weed Zapper™.
Figure 5 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 5. Weed biomass in each weed management treatment pooled across all site-years.Biomass was sampled in mid-August after all management tactics had been applied. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 4 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 4. Soybean density in August after all weed management treatments were applied. Data were pooled across all site-years. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 8 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 8. Soybean yield from each weed management treatment pooled across fields. Yield is dry weight corrected to 13% moisture. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 1 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 1. The interrow mower used in this experiment, attached to a John DeereṜ 5100R tractor with a three-point hitch. The mower is powered with a hydraulic system and was custom made by IRM X4, R-Tech Industries (Homewood, MB, Canada).
Figure 2. The model 6R30 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 2. The model 6R30 Weed Zapper™ used in this experiment. The generator is attached to the back of a John DeereṜ 5100R tractor with a three-point hitch. The 4.6-m electric copper boom is attached to the front of the tractor with a three-point hitch. The Weed Zapper™ was purchased from Old School Manufacturing (Sedalia, MO, USA).
Figure 3 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 3. Monthly temperature and precipitation in Aurora, NY, USA, in 2021 and 2022. Pink lines indicate 30-yr average.
CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis
<div>This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well. </div>
Interior Alaska managed sites: soil organic layer and mineral soil nutrient concentrations and additional characteristics measured one time in either summer 2012 or 2013
This dataset contains soil organic layer and mineral soil depth, C and N concentrations, and other soil characteristics collected at all managed and adjacent unmanaged study sites.
Interior Alaska managed sites: soil organic layer depth and thaw depth measured one time in either summer 2012 or 2013
This dataset contains estimates of soil organic layer depth and thaw depth along transects measured at a suite of managed and unmanaged areas in interior Alaska in summers 2012 and 2013.
Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. </p> <p>These data correspond to the open-access articles:</p> <p>- Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2</p> <p>- Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297</p> <p>- Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2</p> <p>The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Data from: Contrasting patterns of functional diversity in coffee root fungal communities associated with organic and conventionally-managed fields
<p>The structure and function of fungal communities in the coffee rhizosphere is shaped by crop environment. Because coffee can be grown along a management continuum from conventional application of pesticides and fertilizers in full sun to organic management in a shaded understory, we used coffee fields to hold host constant while comparing rhizosphere fungal communities in markedly different environmental conditions with regard to shade and inputs. We characterized the shade and soil environment in 25 fields under conventional, organic or transitional management in two regions of Costa Rica. We amplified the ITS2 region of fungal DNA from coffee roots in these fields and characterized the rhizosphere fungal community via high-throughput sequencing. Sequences were assigned to guilds to determine differences in functional diversity and trophic structure among coffee field environments. Organic fields had more shade, a greater richness of shade tree species, more leaf litter, and were less acidic, with lower soil nitrate availability and higher soil copper, calcium, and magnesium than conventionally-managed fields, although differences between organic and conventionally-managed fields in shade, calcium and magnesium depended on region. Differences in richness and community composition of rhizosphere fungi between organic and conventionally-managed fields were also correlated with shade, soil acidity, nitrate, and copper. Trophic structure differed with coffee field management. Saprotrophs, plant pathogens, and mycoparasites were more diverse and plant pathogens were more abundant in organic than in conventionally-managed fields, while saprotroph-plant pathogens were more abundant in conventionally-managed fields. These differences reflected environmental differences and depended on region.</p> <p><b>IMPORTANCE</b></p> <p>Rhizosphere fungi play key roles in ecosystems, as nutrient cyclers, pathogens, and mutualists, yet little is currently known about which environmental factors and how agricultural management shape rhizosphere fungal communities and their functional diversity. This field study of the coffee agroecosystem suggests that organic management not only fosters a greater overall diversity of fungi, but also maintains a greater richness of saprotrophic, plant pathogenic and mycoparasitic fungi that has implications for efficiency of nutrient cycling and regulation of plant pathogen populations in agricultural systems. As well as influencing community composition and richness of rhizosphere fungi, shade management and use of fungicides and synthetic fertilizers altered the trophic structure of the coffee agroecosystem.</p>
Evaluation of the administrative management model in a municipality in Peru, incorporating the Intelligent Organization Theory
<p><strong>Background:</strong> This study begins with the analysis of the current management models and their degree of effectiveness in municipal administration. Its aim is to design an administrative management model that enables effective administration in the District Municipality of Nueva Cajamarca, Peru, based on the theory of intelligent organizations.</p><p><strong>Method: </strong>The research type employed in this study is diagnostic-propositional, utilizing both deductive and inductive methods, in alignment with a mixed-method approach and a non-experimental nature of the study. Data was collected from three distinct populations, including the 189 municipal employees engaged in administrative roles, who were subjected to a 50-question survey. This survey aimed to assess their perceptions regarding the current management model and its relationship with administrative effectiveness. Additionally, interviews were conducted with three experts to gain deeper insights into the behavior of the variables under investigation.</p><p><strong>Results: </strong>Finally, documentary information about the management models currently in use was collected. This facilitated the triangulation of data collection, processing, analysis, and inferences from three sources of information. The results reveal a positive, direct, and significant correlation between the management model and administrative effectiveness. It becomes evident that the current management model is deficient, resulting in a low level of administrative effectiveness</p><p><strong>Conclusion: </strong></p><p>The management model based on the theory of intelligent organizations was validated using a rubric by experts in effective management. The main pillars of this model include transformational leadership, structural change, and cultural change.</p>
Dataset associated with the manuscript: Soil management legacy interacts with wheat genotype to determine access to organic N in a dryland system
<p>Organic nutrient management through the application of compost and/or cover crops provides mineralizable sources of nutrients for plants while often building soil organic matter (SOM) and various aspects of soil health. Variability in nutrient acquisition strategies between crop genotypes may confer advantages under different soil health contexts and could be important for crop selection and breeding, but crop response under field conditions remains unexplored. We investigated the ability of different genotypes of winter wheat (<em>Triticum aestivum</em> L.) to access nitrogen (N) from newly added cover crop residues in two soils with contrasting levels of SOM and biological activity. We planted three previously characterized wheat genotypes in a long-term dryland compost amendment field trial: 1) Byrd (modern, deep roots, low exudation), 2) Cheyenne (historic, drought susceptible, intermediate exudation), and 3) Snowmass (modern, drought-susceptible, high exudation). <sup>15</sup>N-labelled cover crop residue was added to each plot and traced into wheat tissue. In the low SOM soil, the high exudate genotype Snowmass and historic genotype Cheyenne took up the most residue-derived N (6.4-8.1 kg N ha<sup>-1</sup>) compared to the low-exudate genotype Byrd (4.4 kg N ha<sup>-1</sup>), suggesting a strong exudate effect in the more carbon-limited soil. However, the low-exudate, deep rooted genotype, Byrd, took up the most residue N in the high SOM soils (4.6 kg N ha<sup>-1 </sup>vs. 2.8 and 3.3 hg N ha<sup>-1</sup> for Cheyenne and Snowmass, respectively), which indicated higher native N cycling activities and great importance of drought resistance. Enzyme activity, inorganic N, and microbial communities were not influenced by genotype, though did show strong effects of compost application legacy. Our results show that belowground allocation strategies that favor microbial stimulation may be less successful under water limitation, especially when high SOM can support mineralization of residue N without added investment in root inputs. Increased soil health through SOM-building management likely enhances nutrient cycling, and may better support root strategies that invest less in microbial stimulation in favor of other limiting resources.</p>
The influence of inherent soil factors and agricultural management on soil organic matter
<p>The accumulation of soil organic matter (SOM) is vital to the agronomic and environmental functioning of agroecosystems, yet the relative influence of inherent soil properties and agricultural management practices on SOM dynamics are not often addressed in individual studies. Using a network of 218 operating farm fields across Wisconsin and southern Minnesota, USA, this research employs single variable analysis (ANOVA and regression) and regression tree analysis to assess the effects of soil properties (texture, drainage class, pH) and management variables related to crop rotation, tillage, cover cropping, and manure application on SOM, as well as total organic carbon (TOC) and total nitrogen (TN) in the upper 15 cm. Single variable analysis revealed that greater SOM, TOC, and TN were associated with poorly drained soil, tile-drained fields, high-clay content soil, and high biomass crop rotations. Soil organic matter (SOM) and TOC were strongly related (R<sup>2</sup>=0.71), but different regression trees were produced; SOM was most influenced by clay content, while TOC was most influenced by drainage class. Future assessment for the building of SOM or TOC should be conducted with drainage and texture class categories and on a regional basis, given that these factors influence the practices that occur within landscapes. A rapid building of data sets through unstructured sampling, including an abundance of meta-data, should be a research priority in agricultural science to identify practices to build SOM on a regional basis.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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