Creating a Dynamic Regional Model of the U.S. Corn Belt

2017 ◽  
Vol 8 (4) ◽  
pp. 19-29 ◽  
Author(s):  
Christopher R. Laingen
Keyword(s):  
Agricultural Land ◽  
Driving Forces ◽  
Corn Belt ◽  
Farming Practices ◽  
Livestock Farming ◽  
Department Of Agriculture ◽  
Corn Production ◽  
Regional Boundary ◽  
The U.S ◽  
External Driving

The U.S. Department of Agriculture last delineated the regional boundary of the Corn Belt in 1950. Mixed-grain and livestock farming practices have today transitioned to annually rotating corn and soybeans, which has altered the geographic bounds of this region. To illustrate the changing geography of the Corn Belt, ArcGIS geoprocessing and spatial analysis tools, along with a simple, summative assessment using Census of Agriculture data, were used to map how the region's boundary has changed as myriad internal and external driving forces influence where farmers grow corn. Since 1950 the region's core has remained spatially stable as corn production has intensified, while the region's periphery has shifted to the northwest. The methods used to create this contemporary Corn Belt region illustrate how a regional boundary and internal regional intensities can be used to map agricultural land use change.

scholarly journals Climate Variability and the U.S. Corn Belt: ENSO and AO Episode-Dependent Hydroclimatic Feedbacks to Corn Production at Regional and Local Scales*

2015 ◽  
Vol 19 (6) ◽  
pp. 1-32 ◽  
Author(s):  
Olivia Kellner ◽  
Dev Niyogi
Keyword(s):  
El Niño ◽  
El Nino ◽  
Growing Season ◽  
Yield Potential ◽  
Corn Belt ◽  
Corn Yield ◽  
Corn Production ◽  
Enso Events ◽  
Temperature And Precipitation ◽  
The U.S

Abstract El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980–2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt. This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production.

scholarly journals Landsat-based Irrigation Dataset (LANID): 30-m resolution maps of irrigation distribution, frequency, and change for the U.S., 1997–2017

2021 ◽  
Author(s):  
Yanhua Xie ◽  
Holly K. Gibbs ◽  
Tyler J. Lark
Keyword(s):  
Reference Data ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Irrigation Frequency ◽  
Department Of Agriculture ◽  
Time Step ◽  
Field Level ◽  
Period Derivative ◽  
Abstract Data ◽  
The U.S

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents is vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the U.S. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30-m resolution annual irrigation maps covering the conterminous U.S. (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5 million hectares of croplands and pasture/hay have been irrigated, among which the yearly active area ranged from ~22.6 to 24.7 million hectares. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ~10,000 visually interpreted ground reference locations for the eastern U.S. where such data has been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern U.S., marking a significant advancement over other products, whose accuracies ranged from 50 to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with U.S. Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation especially in challenging areas like the eastern U.S. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5003976 (Xie et al., 2021).

scholarly journals A Place-Based Approach to Agricultural Nonmaterial Intangible Cultural Ecosystem Service Values

2020 ◽  
Vol 12 (2) ◽  
pp. 699 ◽  
Author(s):  
Joy R. Petway ◽  
Yu-Pin Lin ◽  
Rainer F. Wunderlich
Keyword(s):  
Land Cover ◽  
Ecosystem Service ◽  
Agricultural Landscape ◽  
Agricultural Land ◽  
Environmental Data ◽  
Farming Practices ◽  
Environmental Characteristics ◽  
Cultural Ecosystem Service ◽  
Context Specific ◽  
Landscape Biodiversity

Though agricultural landscape biodiversity and ecosystem service (ES) conservation is crucial to sustainability, agricultural land is often underrepresented in ES studies, while cultural ES associated with agricultural land is often limited to aesthetic and tourism recreation value only. This study mapped 7 nonmaterial-intangible cultural ES (NICE) valuations of 34 rural farmers in western Taiwan using the Social Values for Ecosystem Services (SolVES) methodology, to show the effect of farming practices on NICE valuations. However, rather than a direct causal relationship between the environmental characteristics that underpin ES, and respondents’ ES valuations, we found that environmental data is not explanatory enough for causality within a socio-ecological production landscape where one type of land cover type (a micro mosaic of agricultural land cover) predominates. To compensate, we used a place-based approach with Google Maps data to create context-specific data to inform our assessment of NICE valuations. Based on 338 mapped points of 7 NICE valuations distributed among 6 areas within the landscape, we compared 2 groups of farmers and found that farmers’ valuations about their landscape were better understood when accounting for both the landscape’s cultural places and environmental characteristics, rather than environmental characteristics alone. Further, farmers’ experience and knowledge influenced their NICE valuations such that farm areas were found to be sources of multiple NICE benefits demonstrating that farming practices may influence ES valuation in general.

scholarly journals Solitary waves in the nonlinear Dirac equation in the presence of external driving forces

2016 ◽  
Vol 49 (6) ◽  
pp. 065402 ◽  
Author(s):  
Franz G Mertens ◽  
Fred Cooper ◽  
Niurka R Quintero ◽  
Sihong Shao ◽  
Avinash Khare ◽  
...  
Keyword(s):  
Dirac Equation ◽  
Solitary Waves ◽  
Driving Forces ◽  
Nonlinear Dirac Equation ◽  
External Driving

scholarly journals Estimating a global demand model for soybean traffic through the Panama Canal

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Javier Ho ◽  
Paul Bernal
Keyword(s):  
Pacific Northwest ◽  
Least Square ◽  
Panama Canal ◽  
Demand Model ◽  
Department Of Agriculture ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Global Demand ◽  
Alternative Routes ◽  
The U.S

AbstractThis study attempts to fit a global demand model for soybean traffic through the Panama Canal using Ordinary Least Square. Most of the soybean cargo through the interoceanic waterway is loaded on the U.S. Gulf and East Coast ports -mainly destined to East Asia, especially China-, and represented about 34% of total Panama Canal grain traffic between fiscal years 2010–19. To estimate the global demand model for soybean traffic, we are considering explanatory variables such as effective toll rates through the Panama Canal, U.S. Gulf- Asia and U.S. Pacific Northwest- Asia freight rates, Baltic Dry Index, bunker costs, soybean export inspections from the U.S. Gulf and Pacific Northwest, U.S. Gulf soybean basis levels, Brazil’s soybean exports and average U.S. dollar index. As part of the research, we are pursuing the estimation of the toll rate elasticity of vessels transporting soybeans via the Panama Canal. Data come mostly from several U.S. Department of Agriculture sources, Brazil’s Secretariat of Foreign Trade (SECEX) and from Panama Canal transit information. Finally, after estimation of the global demand model for soybean traffic, we will discuss the implications for future soybean traffic through the waterway, evaluating alternative routes and sources for this trade.

scholarly journals Resistance to Bt Corn by Western Corn Rootworm (Coleoptera: Chrysomelidae) in the U.S. Corn Belt

2013 ◽  
Vol 4 (3) ◽  
pp. 1-6 ◽  
Author(s):  
Eileen M. Cullen ◽  
Michael E. Gray ◽  
Aaron J. Gassmann ◽  
Bruce E. Hibbard
Keyword(s):  
Western Corn Rootworm ◽  
Corn Belt ◽  
Bt Corn ◽  
Corn Rootworm ◽  
The U.S

scholarly journals Nonstationary time series prediction combined with slow feature analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 377-382 ◽  
Author(s):  
G. Wang ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Driving Forces ◽  
Time Series Prediction ◽  
Nonstationary Time Series ◽  
Feature Analysis ◽  
State Variables ◽  
Slow Feature Analysis ◽  
Almost All ◽  
External Driving

Abstract. Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

scholarly journals History of Bermudagrass Turfgrass Breeding Research in Tifton, GA

HortScience ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1560-1561 ◽  
Author(s):  
Lisa L. Baxter ◽  
Brian M. Schwartz
Keyword(s):  
Coastal Plain ◽  
Agricultural Research ◽  
Department Of Agriculture ◽  
University Of Georgia ◽  
Breeding Research ◽  
History Of ◽  
The University ◽  
Warm Temperate ◽  
Agricultural Research Service ◽  
The U.S

Bermudagrass (Cynodon spp.) is the foundation of the turfgrass industry in most tropical and warm-temperate regions. Development of bermudagrass as a turfgrass began in the early 1900s. Many of the cultivars commercially available today have been cooperatively released by the U.S. Department of Agriculture Agricultural Research Service (USDA-ARS) and the University of Georgia at the Coastal Plain Experiment Station in Tifton, GA.

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