Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

The nexus of value chain integration and e-business applications on public sector agriculture R&D management

We examine the potential impact of interconnectivity of value chain partnerships through electronic means (e-business practices) on the management of Public Sector Agriculture R&D in Australia. We review the changing forms of managing research and development, the forces driving these changes, and R&D processes that are theoretically consistent with the move towards value chain involvement and the increase in active constituents in Public Sector Agriculture R&D. We then explore the potential of emerging e-business models to change the patterns of inter-connectivity, speed and omnipresence of partners in the value chain. Three e-business R&D management practices are identified that provide the prerequisite flexibility necessary to take advantage of opportunistic markets. These R&D business practices are: compressing R&D to reduce time to market, fostering co-development to enter a market at the last moment and building flexible products that allow adjustment at the last possible moment. Some fundamental reallocation of existing resources will be required to meet these markets. Implications of these e-business practices for R&D management are discussed.

Productivity growth in pre-1990 Mongolian agriculture: Spiralling disaster or emerging success?

The reasons for the spectacular collapse of so many centrally-planned economies are a source of ongoing debate. In this paper, we use detailed farm-level data to measure total factor productivity (TFP) changes in Mongolian grain and potato farming during the 14-year period immediately preceding the 1990 economic reforms. We measure TFP growth using stochastic frontier analysis (SFA) and data envelopment analysis (DEA) methods. Our results indicate quite poor overall performance, with an average annual TFP change of - 1.7% in grain and 0.8% in potatoes, over the 14-year period. However, the pattern of TFP growth changed substantially during this period, with TFP growth exceeding 7% per year in the latter half of this period. This suggests that the new policies of improved education, greater management autonomy, and improved incentives, which were introduced in final two planning periods in the 1980s, were beginning to have a significant influence upon the performance of Mongolian crop farming. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

O PRONAF e os seus reflexos para a agricultura familiar

O presente trabalho busca através do estudo da modernização da agricultura, com foco no período de 1965-2012, compreender o papel do Estado na formulação de políticas agrícolas que conduz o desenvolvimento do modo de produção capitalista na agricultura. Utiliza-se como fonte de pesquisa dados secundários de agências governamentais, supranacionais e autores com tradição no estudo do tema, a fim de dissertar sobre as particularidades do desenvolvimento do capitalismo na agricultura em um país de economia dependente e subdesenvolvido. Para isso, faz-se necessário compreender o controle exercido pelo capital internacional sobre os elos estratégicos da economia, a perpetuação da segregação social e a superexploração do trabalho como base da sociedade nacional. Conclui-se que o Programa Nacional de Fortalecimento da Agricultura Familiar (PRONAF), política agrícola que surge em meados da década de 1990, criado para pequenos produtores, vem promovendo feitos similares à política agrícola formulada pelo Estado no período do regime militar que ficou classificado como modernização conservadora. Nesta direção, o PRONAF apresenta como estratégia o esforço de enquadrar agricultores familiares nos paradigmas da eficiência produtiva, que acaba resultando entre aqueles aptos (competitivos) e não aptos para a sobrevivência no mercado

Azorean Agriculture Efficiency by PAR

Copyright © 2013 Springer Netherlands.

Sustainable Tourism and Agriculture Multifunctionality by PAR: A Variable Selection Approach

Copyright © 2013 Springer Netherlands.

The debate on health in Portugal: governance of health care and the welfare modernization agenda

Comunicação apresentada na "Second Biennial Conference of the Standing Group on Regulation and Governance do ECPR Regulation & Governance, com o tema: (Re)Regulation in the Wake of Neoliberalism’ Consequences of Three Decades of Privatization and Market Liberalization, realizada na Universidade de Utrecht, the Netherlands, de 5 a 7 de Junho de 2008.

Bridging environmental conflicts with social metabolism : forestry expansion and socioeconomic change

Dissertação apresentada para obtenção do Grau de Doutor em Ciências do Ambiente, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

New perspectives on the history of brazilian agriculture.

One of the objectives of this book has been to highlight the importance of the history of agriculture in today's food production and consumption patterns, recovering parts of this history which are not easily found or which have been forgotten or neglected, but which have had a large impact on what we are today.

DESARROLLO DE APLICACIONES ESTADÍSTICAS PARA LA AGRICULTURA DE PRECISIÓN. STATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE

A partir de las últimas décadas se ha impulsado el desarrollo y la utilización de los Sistemas de Información Geográficos (SIG) y los Sistemas de Posicionamiento Satelital (GPS) orientados a mejorar la eficiencia productiva de distintos sistemas de cultivos extensivos en términos agronómicos, económicos y ambientales. Estas nuevas tecnologías permiten medir variabilidad espacial de propiedades del sitio como conductividad eléctrica aparente y otros atributos del terreno así como el efecto de las mismas sobre la distribución espacial de los rendimientos. Luego, es posible aplicar el manejo sitio-específico en los lotes para mejorar la eficiencia en el uso de los insumos agroquímicos, la protección del medio ambiente y la sustentabilidad de la vida rural. En la actualidad, existe una oferta amplia de recursos tecnológicos propios de la agricultura de precisión para capturar variación espacial a través de los sitios dentro del terreno. El óptimo uso del gran volumen de datos derivado de maquinarias de agricultura de precisión depende fuertemente de las capacidades para explorar la información relativa a las complejas interacciones que subyacen los resultados productivos. La covariación espacial de las propiedades del sitio y el rendimiento de los cultivos ha sido estudiada a través de modelos geoestadísticos clásicos que se basan en la teoría de variables regionalizadas. Nuevos desarrollos de modelos estadísticos contemporáneos, entre los que se destacan los modelos lineales mixtos, constituyen herramientas prometedoras para el tratamiento de datos correlacionados espacialmente. Más aún, debido a la naturaleza multivariada de las múltiples variables registradas en cada sitio, las técnicas de análisis multivariado podrían aportar valiosa información para la visualización y explotación de datos georreferenciados. La comprensión de las bases agronómicas de las complejas interacciones que se producen a la escala de lotes en producción, es hoy posible con el uso de éstas nuevas tecnologías. Los objetivos del presente proyecto son: (l) desarrollar estrategias metodológicas basadas en la complementación de técnicas de análisis multivariados y geoestadísticas, para la clasificación de sitios intralotes y el estudio de interdependencias entre variables de sitio y rendimiento; (ll) proponer modelos mixtos alternativos, basados en funciones de correlación espacial de los términos de error que permitan explorar patrones de correlación espacial de los rendimientos intralotes y las propiedades del suelo en los sitios delimitados. From the last decades the use and development of Geographical Information Systems (GIS) and Satellite Positioning Systems (GPS) is highly promoted in cropping systems. Such technologies allow measuring spatial variability of site properties including electrical conductivity and others soil features as well as their impact on the spatial variability of yields. Therefore, site-specific management could be applied to improve the efficiency in the use of agrochemicals, the environmental protection, and the sustainability of the rural life. Currently, there is a wide offer of technological resources to capture spatial variation across sites within field. However, the optimum use of data coming from the precision agriculture machineries strongly depends on the capabilities to explore the information about the complex interactions underlying the productive outputs. The covariation between spatial soil properties and yields from georeferenced data has been treated in a graphical manner or with standard geostatistical approaches. New statistical modeling capabilities from the Mixed Linear Model framework are promising to deal with correlated data such those produced by the precision agriculture. Moreover, rescuing the multivariate nature of the multiple data collected at each site, several multivariate statistical approaches could be crucial tools for data analysis with georeferenced data. Understanding the basis of complex interactions at the scale of production field is now within reach the use of these new techniques. Our main objectives are: (1) to develop new statistical strategies, based on the complementarities of geostatistics and multivariate methods, useful to classify sites within field grown with grain crops and analyze the interrelationships of several soil and yield variables, (2) to propose mixed linear models to predict yield according spatial soil variability and to build contour maps to promote a more sustainable agriculture.

Report of the State board of agriculture on the work of extermination of the gypsy moth.

1898:Jan.