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19 results for “agricultural economics”
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
The ecological and economic benefits of sustainable agricultural practices
<p><strong>Code and data for Mata et al.'s <em>The ecological and economic benefits of sustainable agricultural practices: Evidence from on-farm trials in broad-acre crops. </em></strong></p> <p><strong>Abstract</strong></p> <p>1. A transition to more sustainable agricultural practices is essential to mitigate the negative environmental impacts of conventional farming and to ensure long-term food security. However, widespread adoption requires robust evidence demonstrating their efficacy and economic viability.</p> <p>2. We co-designed a two-year field trial with farmers and agronomy advisors in Australia to evaluate the ecological and economic outcomes of sustainable agricultural practices for managing the redlegged earth mite, a major pest of Australian crops and pastures. We compared 'Novel' treatments – representing long-term farmer-implemented sustainable practices based on biological control – with 'Conventional' treatments, and 'Plus' treatments designed as counterfactuals to disentangle the effects of specific pest control and plant nutrient components.</p> <p>3. Redlegged earth mite densities remained below economic thresholds across all treatments and years, demonstrating effective pest control in both conventional and sustainable systems. Notably, the Novel treatment supported higher densities of beneficial arthropods, suggesting increased biological control potential.</p> <p>4. Yield and gross profit margins were generally similar across treatments, indicating that sustainable agriculture practices can maintain profitability while fostering biodiversity.</p> <p>Practical implication. Our study provides evidence that biological control and biofertiliser supplementation can be effectively used to manage agricultural pests. It also demonstrates the value of close collaboration with farmers and agronomy advisors in conducting ecological field research with real-world applications.</p>
Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy (dataset)
<p>Dataset inherent to the following article:</p> <p>Medici, M., Canavari, C., Castellini, A., 2021. <em>Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy</em>, Journal of Cleaner Production, 316, 128233, DOI: <a href="https://doi.org/10.1016/j.jclepro.2021.128233">10.1016/j.jclepro.2021.128233</a></p>
Supplementary dataset for "Global agricultural economic water scarcity"
<p>This repository contains supporting data for: "<strong>Global agricultural economic water scarcity"</strong></p> <p>Cite: Rosa, L., Chiarelli, D.D., Rulli, M.C., Dell’Angelo J., and D’Odorico, P. Global agricultural economic water scarcity. Science Advances. 2020<br> Email: lorenzo_rosa@berkeley.edu</p> <p>The dataset contains the number of months (#months) croplands are facing green water scarcity (GWS), blue water scarcity (BWS), and economic water scarcity (EWS). Where "0" indicates that the pixel does not face water scarcity, "1" indicates that the pixel is facing water scarcity for 1 month, "12" indicates that the pixel is facing water scarcity for 12 months. Files are uploaded in arcmap and netcdf formats. </p> <p> </p>
THE IMPACT OF LOCAL PRODUCT BRANDING ON THE ECONOMIC PERFORMANCE OF AGRICULTURAL AND LIVESTOCK AGRO-PROCESSING INDUSTRIES IN VLORA
<p>This study aims to examine the impact of local product branding on the economic performance of the agricultural and livestock agro-processing industries in the region of Vlora. Based on the data collected from local agro-processing and agro-tourism industries, the purpose of this research is to analyze how branding strategies contribute to increasing the level of sales, the level of income, creating a strong identity, and improving the performance of businesses and the territory where these businesses are concentrated. The study includes in the analysis the influence of the local brand in the creation of the identity, in the level of sales, in the income, the profit margins, and the improvement of the economic performance of the agricultural and livestock agro-processing industries. For this reason, the research was conducted with the inclusion of over 100 industries/agritourism, and the analysis of the questionnaire data was conducted with the STATA program. From the results of the research, it is clear that investment in the branding of products with local indicators is necessary to stimulate economic development and to strengthen the market positioning of businesses in the agricultural and livestock sectors. Also, this research provides important recommendations for improving branding practices as a strategic tool for the development and consolidation of agro-processing industries in the region.</p>
Figure 3 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 3 The Land Systems Archetypes mapped by Levers et al. (2018) for one of BESTMAP case study areas in Catalonia. Land Systems Archetypes were derived at 3km resolution for Europe based on CORINE land cover, downscaled ecological and economic data. BESTMAP aims to map Farming System Archetypes (FSAs) at the farm level.
Figure 7 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 7 BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the "Proto-FSAs" clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share "socio-economic-environmental" space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.
Figure 9 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 9 BESTMAP dashboard mock-up, visualizing the impact of the policies through the policy indicators.
Figure 8 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 8 BESTMAP ABM concept. Individual farmer's decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer's attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer's behaviour) and perceived behavioural control (agent's capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm's fields and provide some economic benefit to the farmer. Specifically, farmer's decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.
Figure 4 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 4 BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers ("Case Study Base Layers"; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define 'prototype' Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The 'bundle' of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA 'bundles' including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.
Figure 1 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 1 Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by formalizing engagement with stakeholders to define scenarios; using existing geospatial data and empirical data collection to map farming systems based on a novel concept of Farming System Archetypes (FSA) namely farms with a characteristic bundle of ESS, biodiversity, socio-economics and behavioural characteristics of decision-making agents (i.e. farmers); linking economic (typically global) large scale economic based PE/CGE to agent-based models; describing changes in FSAs's distribution, ESS, biodiversity and socio-economics in a representative sample of case study areas.
Figure 5 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 5 BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by Levers et al. 2018 at 3km resolution for Europe. No land system archetypes shown for Serbia as the mapping included EU countries only. Insets show the exact boundaries of CS areas.
Figure 2 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 2 The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from Erb et al. 2013 to include the land manager/farmer and its behavioural characteristics (based here on the RAA). FSA have a typical 'bundle' of ESS, outputs, outcomes, inputs and farmer characteristics.
The Economic and Environmental Benefits of Partial Leasing of Agricultural Water Rights
<p>Code and data used for partial leasing study</p>
THE ROLE OF ECONOMIC REFORMS IN THE AGRICULTURAL SECTOR OF OUR COUNTRY IN INCREASING THE EXPORT POTENTIAL OF OUR COUNTRY.
Open the record for dataset details and reuse information.
Supplementary material 1 from: Strokov AS, Potashnikov VY (2022) Environmental tradeoffs of agricultural growth in Russian regions and possible sustainable pathways for 2030. Russian Journal of Economics 8(1): 60-80. https://doi.org/10.32609/j.ruje.8.78331
Maps of main environmental indicators of Russian regional agricultural development
Supplementary material 2 from: Strokov AS, Potashnikov VY (2022) Environmental tradeoffs of agricultural growth in Russian regions and possible sustainable pathways for 2030. Russian Journal of Economics 8(1): 60-80. https://doi.org/10.32609/j.ruje.8.78331
The dataset on agricultural waste, nitrogen concentration, and GHG emissions in Russian regions
Remote Sensing Derived Topsoil and Agricultural Economic Losses, Midwestern USA
This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site.The relationship between topsoil loss and topographic curvature was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.
Figure 6 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 6 Typical agricultural landscapes of BESTMAP demonstration case studies.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.