Rethinking study and management of agricultural systems for policy design

There is a concern that agriculture will no longer be able to meet, on a global scale, the growing demand for food. Facing such a challenge requires new patterns of thinking in the context of complexity and sustainability sciences. This paper, focused on the social dimension of the study and management of agricultural systems, suggests that rethinking the study of agricultural systems entails analyzing them as complex socio-ecological systems, as well as considering the differing thinking patterns of diverse stakeholders. The intersubjective nature of knowledge, as studied by different philosophical schools, needs to be better integrated into the study and management of agricultural systems than it is done so far, forcing us to accept that there are no simplistic solutions, and to seek a better understanding of the social dimension of agriculture. Different agriculture related problems require different policy and institutional approaches. Finally, the intersubjective nature of knowledge asks for the visualization of different framings and the power relations taking place in the decision-making process. Rethinking management of agricultural systems implies that policy making should be shaped by different principles: learning, flexibility, adaptation, scale-matching, participation, diversity enhancement and precaution hold the promise to significantly improve current standard management procedures.

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2014 and 2013

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2014 and 2013

Two-step postmortem angiography with a modified heart-lung machine: preliminary results.

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.

Heavy Agricultural Loads on Pavements and Bridges, HR-1073, 1999

During the harvest season in Iowa, it is common to have single axle loads on secondary roads and bridges that are excessive (typical examples are grain carts) and well beyond normal load limits. Even though these excessive loads occur only during a short time of the year, they may do significant damage to pavements and bridges. In addition, the safety of some bridges may be compromised because of the excessive loads, and sometimes there may be little indication to the users that damage may be imminent. At this time there are no Iowa laws regulating axle loads allowed for agricultural equipment. This study looks at the potential problems this may cause on secondary roads and timber stringer bridges. Both highway pavement and timber bridges are evaluated in this report. A section (panel) of Iowa PCC paved county road was chosen to study the effects of heavy agricultural loads on pavements. Instrumentation was applied to the panel and a heavily loaded grain cart was rolled across. The collected data were analyzed for any indication of excessive stresses of the concrete. The second study, concerning excessive loads on timber stringer bridges, was conducted in the laboratory. Four bridge sections were constructed and tested. Two of the sections contained five stringers and two sections had three stringers. Timber for the bridges came from a dismantled bridge, and deck panels were cut from new stock. All timber was treated with creosote. A hydraulic load was applied at the deck mid-span using a foot print representing a tire from a typical grain cart. Force was applied until failure of the system resulted. The collected data were evaluated to provide indications of load distribution and for comparison with expected wheel loads for a typical heavily loaded single axle grain cart. Results of the pavement tests showed that the potential of over-stressing the pavement is a possibility. Even though most of the tension stress levels recorded were below the rupture strength of the concrete, there were a few instances where the indicated tension stress level exceeded the concrete rupture strength. Results of the bridge tests showed that when the static ultimate load capacity of the timber stringer bridge sections was reached, there was sudden loss of capacity. Prior to reaching this ultimate capacity, the load sharing between the stringers was very uniform. The failure was characterized by loss of flexural capacity of the stringers. In all tests, the ultimate test load exceeded the wheel load that would be applied by an 875 bushel single axle grain cart.

Water scarcity management and agricultural production (What we can do to produce more with less water)

Crop seasonal sensitivity to water stress is concerned with how to control water stress levels to optimise yield or profitability. It deals with when we can reduce irrigation and impose moderate water deficits without affecting our target, and when we can apply water to avoid too much stress.

Effects of competing vegetation and post-planting weed control on the mortality, growth and vole damages to Betula pendula planted on former agricultural land

Abstract

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2014

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2014

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Iowa Rural and Agricultural Health and Safety Resource Plan, 2011

The Iowa Department of Public Health (IDPH) convened the Health and Long-Term Care Advisory Council (HLTCAC) to assist in the development of its strategic plan. One component of the strategic plan is a rural health care resource plan. The intent of this document is to present reliable information and data as a valuable resource for the officials, agencies, and organizations responsible for strengthening and supporting the rural health systems vital to 43 percent of Iowa residents.

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2015 and 2014

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2015 and 2014

Impacts of climate change on the agricultural zoning of climate risk for cotton cultivation in Brazil

The objective of this work was to evaluate the effect of the temperature increase forecasted by the Intergovernmental Panel on Climate Change (IPCC) on agricultural zoning of cotton production in Brazil. The Northeastern region showed the highest decrease in the low-risk area for cotton cultivation due to the projected temperature increase. This area in the Brazilian Northeast may decrease from 83 million ha in 2010 to approximately 71 million ha in 2040, which means 15% reduction in 30 years. Southeastern and Center-Western regions had small decrease in areas suitable for cotton production until 2040, while the Northern region showed no reduction in these areas. Temperature increase will not benefit cotton cultivation in Brazil because dimension of low-risk areas for economic cotton production may decrease.

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.