scholarly journals Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 66
Author(s):  
Seong Do Yun ◽  
Benjamin M. Gramig
Keyword(s):  
Spatial Data ◽  
Crop Yields ◽  
Original Data ◽  
Data Sources ◽  
Weather Data ◽  
Climatic Data ◽  
Soil Characteristic ◽  
Growing Seasons ◽  
Aggregation And Disaggregation ◽  
Agricultural Yields

Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree days for 1 °C intervals between −60 °C and +60 °C, and total precipitation for two different crop growing seasons from the PRISM weather data are included. Soil characteristic data from USDA-NRCS gSSURGO are also provided for each county in the 48 contiguous US states. All weather and soil data are processed to include only data for land being used for non-forestry agricultural uses based on the USGS NLCD land cover/land use data. This paper explains the numerical and geo-computational methods and data generating processes employed to create ACDC from the original data sources. Essential considerations for data management and use are discussed, including the use of the agricultural mask, spatial aggregation and disaggregation, and the computational requirements for working with the raw data sources.

scholarly journals From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

2005 ◽  
Vol 360 (1463) ◽  
pp. 2095-2108 ◽  
Author(s):  
Christian Baron ◽  
Benjamin Sultan ◽  
Maud Balme ◽  
Benoit Sarr ◽  
Seydou Traore ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Climate Variability ◽  
General Circulation ◽  
Crop Yields ◽  
Grid Cell ◽  
General Circulation Models ◽  
Crop Model ◽  
Weather Data ◽  
Climatic Data ◽  
Cell Means

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

2021 ◽  
Vol 12 (3) ◽  
pp. 11-14
Author(s):  
Joon-Seok Kim ◽  
Taylor Anderson ◽  
Ashwin Shashidharan ◽  
Andreas Züfle
Keyword(s):  
Spatial Data ◽  
International Workshop ◽  
Data Sources ◽  
Relative Location ◽  
Technological Changes ◽  
Research Opportunities ◽  
New Methodologies ◽  
New Research

Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

Interspecific root interactions enhance photosynthesis and biomass of intercropped millet and peanut plants

2019 ◽  
Vol 70 (3) ◽  
pp. 234
Author(s):  
Xiaojin Zou ◽  
Zhanxiang Sun ◽  
Ning Yang ◽  
Lizhen Zhang ◽  
Wentao Sun ◽  
...  
Keyword(s):  
Crop Yields ◽  
Positive Root ◽  
Foxtail Millet ◽  
Phosphoglycerate Kinase ◽  
Plant Productivity ◽  
Photosynthetic Rates ◽  
Interspecific Facilitation ◽  
Growing Seasons ◽  
Root Interactions ◽  
Root Barrier

Intercropping is commonly practiced worldwide because of its benefits to plant productivity and resource-use efficiency. Belowground interactions in these species-diverse agro-ecosystems can greatly contribute to enhancing crop yields; however, our understanding remains quite limited of how plant roots might interact to influence crop biomass, photosynthetic rates, and the regulation of different proteins involved in CO2 fixation and photosynthesis. We address this research gap by using a pot experiment that included three root-barrier treatments with full, partial and no root interactions between foxtail millet (Setaria italica (L.) P.Beauv.) and peanut (Arachis hypogaea L.) across two growing seasons. Biomass of millet and peanut plants in the treatment with full root interaction was 3.4 and 3.0 times higher, respectively, than in the treatment with no root interaction. Net photosynthetic rates also significantly increased by 112–127% and 275–306% in millet and peanut, respectively, with full root interaction compared with no root interaction. Root interactions (without barriers) contributed to the upregulation of key proteins in millet plants (i.e. ribulose 1,5-biphosphate carboxylase; chloroplast β-carbonic anhydrase; phosphoglucomutase, cytoplasmic 2; and phosphoenolpyruvate carboxylase) and in peanut plants (i.e. ribulose 1,5-biphosphate carboxylase; glyceraldehyde-3-phosphate dehydrogenase; and phosphoglycerate kinase). Our results provide experimental evidence of a molecular basis that interspecific facilitation driven by positive root interactions can contribute to enhancing plant productivity and photosynthesis.

Can urban pluvial flooding be predicted by open spatial data and weather data?

2016 ◽  
Vol 85 ◽  
pp. 156-171 ◽  
Author(s):  
S. Gaitan ◽  
N.C. van de Giesen ◽  
J.A.E. ten Veldhuis
Keyword(s):  
Spatial Data ◽  
Weather Data ◽  
Pluvial Flooding ◽  
Urban Pluvial Flooding

scholarly journals Spatial data infrastructure for meteorological and climatic data

2005 ◽  
Vol 12 (1) ◽  
pp. 7-8 ◽  
Author(s):  
Frans J. M. van der Wel
Keyword(s):  
Spatial Data ◽  
Spatial Data Infrastructure ◽  
Climatic Data ◽  
Data Infrastructure

Ireland

2010 ◽  
pp. 105-130
Author(s):  
Edward Dwyer ◽  
Kathrin Kopke ◽  
Valerie Cummins ◽  
Elizabeth O’Dea ◽  
Declan Dunne
Keyword(s):  
Reference Material ◽  
Spatial Data ◽  
Data Sources ◽  
Web Gis ◽  
Internet Resource ◽  
Digital Atlas ◽  
Wide Range ◽  
Gis Environment ◽  
Data Layers ◽  
Additional Reference

The Marine Irish Digital Atlas (MIDA) is an Internet resource built in a web GIS environment, where people interested in coastal and marine information for Ireland can visualize and identify pertinent geospatial datasets and determine where to acquire them. The atlas, which is being constantly maintained, currently displays more than 140 data layers from over 35 coastal and marine organizations both within Ireland and abroad. It also features an “InfoPort” which is a repository of text, imagery, links to spatial data sources and additional reference material for a wide range of coastal and marine topics. The MIDA team has been active in the creation of the International Coastal Atlas Network and the Atlas was chosen as one of the nodes for the Semantic Interoperability Demonstrator.

scholarly journals CitySAC: A Query-Able CityGML Compression System

Smart Cities ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 106-117
Author(s):  
Chengxi Siew ◽  
Pankaj Kumar
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
City Planning ◽  
Data Transfer ◽  
Original Data ◽  
File Size ◽  
Compression Technique ◽  
3D Data ◽  
Data Infrastructures ◽  
Data Efficiency

Spatial Data Infrastructures (SDIs) are frequently used to exchange 2D & 3D data, in areas such as city planning, disaster management, urban navigation and many more. City Geography Mark-up Language (CityGML), an Open Geospatial Consortium (OGC) standard has been developed for the storage and exchange of 3D city models. Due to its encoding in XML based format, the data transfer efficiency is reduced which leads to data storage issues. The use of CityGML for analysis purposes is limited due to its inefficiency in terms of file size and bandwidth consumption. This paper introduces XML based compression technique and elaborates how data efficiency can be achieved with the use of schema-aware encoder. We particularly present CityGML Schema Aware Compressor (CitySAC), which is a compression approach for CityGML data transaction within SDI framework. Our test results show that the encoding system produces smaller file size in comparison with existing state-of-the-art compression methods. The encoding process significantly reduces the file size up to 7–10% of the original data.

The impact of weather data sources on building energy retrofit design: case study in heating-dominated climate of Italian backcountry

2020 ◽  
Vol 13 (3) ◽  
pp. 264-284 ◽  
Author(s):  
Rosa Francesca De Masi ◽  
Antonio Gigante ◽  
Silvia Ruggiero ◽  
Giuseppe Peter Vanoli
Keyword(s):  
Building Energy ◽  
Data Sources ◽  
Weather Data ◽  
Energy Retrofit ◽  
Design Case ◽  
Design Case Study ◽  
The Impact ◽  
Retrofit Design
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