scholarly journals Biome-scale woody encroachment threatens conservation potential and sustainability of U.S. rangelands

2021 ◽  
Author(s):  
Scott L Morford ◽  
Brady W. Allred ◽  
Dirac Twidwell ◽  
Matthew O. Jones ◽  
Jeremy D. Maestas ◽  
...  
Keyword(s):  
Public Perception ◽  
The United States ◽  
Tree Cover ◽  
Agricultural Producers ◽  
Land Use Conversion ◽  
Machine Learning Methods ◽  
Row Crop ◽  
Tree Encroachment ◽  
Rural Economies ◽  
Privately Owned

Rangelands of the United States provide ecosystem services that sustain biodiversity and rural economies. Native tree encroachment is a recognized and long-standing conservation challenge to these landscapes, but its impact is often overlooked due to the slow pace of tree invasions and the positive public perception of trees. Here we show that tree encroachment is a dominant change agent in U.S. rangelands; tree cover has increased by more than 77,000 km2 over 30 years, and more than 25% of U.S. rangelands are now experiencing sustained tree cover expansion. Further, we use machine learning methods to estimate the potential herbaceous production (forage) lost to tree encroachment. Since 1990 roughly 300 Tg of herbaceous biomass has been lost, totaling some $5 billion in foregone revenue to agricultural producers. These results suggest that tree encroachment is similar in scale and magnitude to row-crop conversion, another primary cause of rangeland loss in the U.S. Prioritizing conservation efforts to prevent tree encroachment in rangelands can bolster ecosystem and economic sustainability of these landscapes, particularly among privately-owned lands threatened by land-use conversion.

Privatizing Public Lands

1995 ◽  
Author(s):  
Scott Lehmann
Keyword(s):  
National Parks ◽  
Fundamental Problem ◽  
Free Market ◽  
Public Lands ◽  
The United States ◽  
Collective Ownership ◽  
Policy Analysts ◽  
Wildlife Refuges ◽  
Ownership Of Land ◽  
Privately Owned

In the United States, private ownership of land is not a new idea, yet the federal government retains title to roughly a quarter of the nation's land, including national parks, forests, and wildlife refuges. Managing these properties is expensive and contentious, and few management decisions escape criticism. Some observers, however, argue that such criticism is largely misdirected. The fundamental problem, in their view, is collective ownership and its solution is privatization. A free market, they claim, directs privately owned resources to their most productive uses, and privatizing public lands would create a free market in their services. This timely study critically examines these issues, arguing that there is no sense of "productivity" for which it is true that greater productivity is both desirable and a likely consequence of privatizing public lands or "marketizing" their management. Lehmann's discussion is self-contained, with background chapters on federal lands and management agencies, economics, and ethics, and will interest philosophers as well as public policy analysts.

scholarly journals The Trade and Environment Debate: Relevant for Southern Agriculture?

1994 ◽  
Vol 26 (1) ◽  
pp. 108-128 ◽  
Author(s):  
Mary A. Marchant ◽  
Nicole Ballenger
Keyword(s):  
United States ◽  
The United States ◽  
Environmental Standards ◽  
Trade And Environment ◽  
Agricultural Producers ◽  
International Debate ◽  
Policy Issues ◽  
Environment Policy ◽  
Know How ◽  
Trade Measures

AbstractThis paper introduces and briefly discusses the economics of two important trade and environment policy issues--international harmonization of environmental standards and the use of trade measures for environmental purposes. Both issues are likely to generate lively international debate among environmentalists, industry representatives, and trade negotiators over the next few years. As the international community seeks new multilateral rules in these areas, agricultural producers will want to know how they will be affected. Thus, this paper also examines the potential impacts of environmental policy on the competitiveness of commodities unique to the Southern region of the United States.

Development Channelization, Partisanship, and Populism: Possibilities for Rural Renewal in the Death Throes of Coal

2021 ◽  
pp. 073112142110246
Author(s):  
Adam Mayer
Keyword(s):  
United States ◽  
Political Ideology ◽  
Energy System ◽  
The United States ◽  
Coal Industry ◽  
Right Wing ◽  
Donald Trump ◽  
Partisan Polarization ◽  
Rural Economies ◽  
Right Wing Populism

In the last few decades, the United States has experienced several related and significant societal trends—the transition of the energy system away from coal, the intensification of partisan polarization, and the rise of a populist right-wing political ideology, perhaps best exemplified by the election of Donald Trump. We build Gramling and Freudenberg’s little-explored concept of “development channelization” to argue that nostalgic right-wing populism, grievances directed toward the federal government, and partisanship converge to potentially thwart efforts to transition and diversify rural economies. Populist nostalgia and blame are associated with support for expanding the collapsing coal industry but do not predict support for other types of development. There are patterns of partisan polarization in support for extractive industries and wind power, but many development options appear to be relatively nonpartisan. We discuss these findings in terms of populism, nostalgia, partisan polarization, and the potential for rural renewal in the United States.

scholarly journals Global estimation of burned area using MODIS active fire observations

2006 ◽  
Vol 6 (4) ◽  
pp. 957-974 ◽  
Author(s):  
L. Giglio ◽  
G. R. van der Werf ◽  
J. T. Randerson ◽  
G. J. Collatz ◽  
P. Kasibhatla
Keyword(s):  
Vegetation Cover ◽  
Regression Trees ◽  
The United States ◽  
Tree Cover ◽  
Burned Area ◽  
Data Set ◽  
Active Fire ◽  
Terra Modis ◽  
Good Agreement ◽  
Area Data

Abstract. We present a method for estimating monthly burned area globally at 1° spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of fire pixels. Unlike earlier methods, we allow the constant of proportionality to vary as a function of tree and herbaceous vegetation cover, and the mean size of monthly cumulative fire-pixel clusters. In areas undergoing active deforestation, we implemented a subsequent correction based on tree cover information and a simple measure of fire persistence. Regions showing good agreement between predicted and observed burned area included Boreal Asia, Central Asia, Europe, and Temperate North America, where the estimates produced by the regression trees were relatively accurate and precise. Poorest agreement was found for southern-hemisphere South America, where predicted values of burned area are both inaccurate and imprecise; this is most likely a consequence of multiple factors that include extremely persistent cloud cover, and lower quality of the 500-m burned area maps used for calibration. Application of our approach to the nine remaining regions yielded comparatively accurate, but less precise, estimates of monthly burned area. We applied the regional regression trees to the entire archive of Terra MODIS fire data to produce a monthly global burned area data set spanning late 2000 through mid-2005. Annual totals derived from this approach showed good agreement with independent annual estimates available for nine Canadian provinces, the United States, and Russia. With our data set we estimate the global annual burned area for the years 2001-2004 to vary between 2.97 million and 3.74 million km2, with the maximum occurring in 2001. These coarse-resolution burned area estimates may serve as a useful interim product until long-term burned area data sets from multiple sensors and retrieval approaches become available.

Analysis of the results of the Russian agricultural census using machine learning methods

2020 ◽  
pp. 41-48
Author(s):  
Anna Evgenievna Kharitonova ◽  
Alina Alekseevna Sundupey ◽  
Svetlana Skachkova
Keyword(s):  
Machine Learning ◽  
Agricultural Land ◽  
High Accuracy ◽  
Learning Methods ◽  
Agricultural Producers ◽  
Machine Learning Methods ◽  
Agricultural Census ◽  
Qualitative Characteristics ◽  
Machine Learning Models ◽  
Crop And Livestock Production

The article provides a comparative analysis of the results of the Russian Agricultural Census of 2006 and 2016. As a result, there is a decrease in the number of agricultural producers, a decrease in the size of agricultural land and equipment in organizations. Against this background, one can see an increase in the concentration of production in both crop and livestock production. Machine learning models have been built to classify subsidy organizations using Python libraries. The accuracy of the constructed models was up to 86 %, which proves the possibility of their use. In the future, the use of machine learning methods will reduce the number of Russian agricultural census indicators and classify organizations with high accuracy according to qualitative characteristics.

Food vs. fuel — A turning point for bioethanol?

2008 ◽  
Vol 56 (4) ◽  
pp. 429-433 ◽  
Author(s):  
K. Harlander
Keyword(s):  
Agricultural Production ◽  
Public Perception ◽  
Turning Point ◽  
The United States ◽  
The Public ◽  
The Us ◽  
Additional Demand ◽  
Oil Crisis ◽  
Emotional Resistance ◽  
The Eu

Bioethanol is made from sugar- or starch-containing plants that are also used in food production. In the public perception this has led to an emotional resistance against biofuels, which in real terms is not substantiated. Generally biofuels are a political product. Triggered by the oil crisis in the 1970s, fuel ethanol programmes were first launched in Brazil and in the United States. Concerns regarding energy security and sustainability, together with the option of new markets for surplus agricultural production, have led to similar measures in the EU and other countries in recent years. Accordingly, the industry invested heavily in new bioethanol plants — especially in the US — and created an additional demand for maize and wheat, with some record-breaking prices noted in late 2007. A look back into statistics shows a drastic decline in real prices for decades, which have now simply returned to the level of 30 years ago. The grain used for bioethanol is currently only 1.6% in the EU and is therefore unlikely to be the real driver of price development. The European Commission concludes in its review of agricultural markets that Europe can do both: nutrition and biofuels.

scholarly journals Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

2021 ◽  
Vol 11 (23) ◽  
pp. 11227
Author(s):  
Arnold Kamis ◽  
Yudan Ding ◽  
Zhenzhen Qu ◽  
Chenchen Zhang
Keyword(s):  
United States ◽  
Machine Learning ◽  
Additive Model ◽  
Regression Tree ◽  
Predictor Variable ◽  
The United States ◽  
Predictor Variables ◽  
Future Research ◽  
Machine Learning Methods ◽  
Variance Explained

The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed.

scholarly journals MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

2021 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Tree Cover ◽  
Anthropogenic Factors ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Machine Learning Methods

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

scholarly journals An Unlikely Symbiosis: Science and Law

2020 ◽  
Vol 17 (2) ◽  
pp. 27-33
Author(s):  
Evan Miller
Keyword(s):  
Science Communication ◽  
Public Perception ◽  
The United States ◽  
Human Condition ◽  
Forensic Sciences ◽  
Expert Witnesses ◽  
Law System ◽  
Sociology Of Scientific Knowledge ◽  
High Regard ◽  
The Common

Science has historically held a position of high regard in society. Science is intimately connected to law. These disciplines meet in the courtroom. Due to the nature of civil and criminal disputes in the United States, litigators retain expert witnesses to explicate nuanced subjects, including science. Unfortunately, the common law system has not always favored sound science. This paper examines how science and law can work in concert to benefit all people. Some feel that scientists should simply educate courtrooms, but further scrutiny questions the feasibility of this approach. Understanding the sociology of scientific knowledge elucidates this debate and is applied to the forensic sciences. Science and law have the capacity to improve the human condition and increase equity among all people. KEYWORDS: Science Communication; Expert Witnesses; Science; Public Perception; Law; Misinformation

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