Suchergebnisse

Purpose - The purpose of this paper is to explore the economic performance of Norwegian crop farms using a stochastic frontier analysis.Design/methodology/approach - The analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from 455 Norwegian farms specialized in crop production in eastern and central regions of Norway.Findings - The results of the analysis show that the mean efficiency was about 78–81 percent. Farm management practices and socioeconomic factors were shown to significantly affect the economic performance of Norwegian crop farms.Research limitations/implications - Farmers are getting different types of support from the government and the study does not account for the different effects of different kinds of subsidy on cost efficiency. Different subsidies might have different effects on farm performance. To get more informative and useful results, it would be necessary to repeat the analysis with less aggregated data on subsidy payments.Practical implications - One implication for farmers (and their advisers) is that many of them are less efficient than the estimated benchmark (best performing farms). Thus, those lagging behind the best performing farms need to look at the way they are operating and to seek out ways to save costs or increase crop production. Perhaps there are things for lagging farmers to learn from their more productive farming neighbors. For instance, those farmers not practicing crop rotation might be well advised to try that practice.Social implications - For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize farmers appear to bring some benefits in terms of more efficient production that, in turn, increase the supply of some foods so possibly making food prices more affordable.Originality/value - Unlike previous performance studies in the literature, the authors estimated farm-level economic performance accounting for the contribution of both an important farm management practice ...

The accuracy and utility of low-cost PM2.5 sensors was evaluated for measuring spatial variation and modeling population exposure to PM2.5 pollution from domestic wood-heating (DWH) in Armidale, a regional town in New South Wales (NSW), Australia, to obtain estimates of health costs and mortality. Eleven 'PurpleAir' (PA) monitors were deployed, including five located part of the time at the NSW government station (NSWGov) to derive calibration equations. Calibrated PA PM2.5 were almost identical to the NSWGov tapered element oscillating microbalance (TEOM) and Armidale Regional Council's 2017 DustTrak measurements. Spatial variation was substantial. National air quality standards were exceeded 32 times from May-August 2018 at NSWGov and 63 times in one residential area. Wood heater use by about 50% of households increased estimated annual PM2.5 exposure by over eight micrograms per cubic meter, suggesting increased mortality of about 10% and health costs of thousands of dollars per wood heater per year. Accurate real-time community-based monitoring can improve estimates of exposure and avoid bias in estimating dose-response relationships. Efforts over the past decade to reduce wood smoke pollution proved ineffective, perhaps partly because some residents do not understand the health impacts or costs of wood-heating. Real-time Internet displays can increase awareness of DWH and bushfire pollution and encourage governments to develop effective policies to protect public health, as recommended by several recent studies in which wood smoke was identified as a major source of health-hazardous air pollution.

It's five years since government worker Glen Turner was murdered by a farmer in a confrontation over land clearing laws. Media reporting after his death frequently propagated the image of the ""poor farmer" at the mercy of laws enforced by out-of-touch city elites. This narrative of an urban-rural divide reared its head again in recent days, when Nationals leader Michael McCormack derided "inner-city raving lunatics" who linked the bushfire crisis to climate change. Such rhetoric may appeal to a conservative party base or media audience, but does little for rural communities in the long run. As farmers face the ever-worsening impacts of drought and climate change, strong environmental protections are required to protect water and other resources. We must better understand how divisive narratives, often serving political interests, are devised and dispersed.

Livestock producers are facing increasing pressure to reduce the environmental and animal-welfare impacts of production, while also managing the challenge of an increasingly variable climate and diminishing resources. This perspective paper highlights the role for animal scientists to contribute to the sustainability of future livestock systems. We argue the need for a broader, more inclusive and more integrated concept of animal science, better connections among scientists, producers, consumers and policy makers, and more support for the next generation of animal scientists. Animal scientists have an important role to play in providing the evidence to support the social licence of livestock production and inform decisions made by policy makers and consumers regarding the production and consumption of livestock products. Animal scientists can also assist producers to adapt to social, environmental and political challenges that affect their livelihoods and the way they farm. Traditionally, animal science has focussed on species- and discipline-specific areas of research such as ruminant nutrition, genetics or reproductive physiology. While this fundamental research remains essential to understand the underlying biology of livestock production and improve production efficiency, it needs to be better integrated into research applied at and beyond the herd or flock level. Systems thinkers who can apply this knowledge across farm, regional and national scales also have an important role in providing information to key decision makers, from farmers to national government. Better engagement with the social and economic sciences can inform how animal scientists and extension services interact with producers to understand constraints to production as well as adoption of new technology and co-develop evidence-based solutions. Underlying this, the demographics of those who study and work in animal science are changing. Australian animal industries require the best and brightest minds to overcome future challenges and ...

Available georeferenced environmental layers are facilitating new insights into global environmental assets and their vulnerability to anthropogenic inputs. Geographically gridded data of agricultural pesticides are crucial to assess human and ecosystem exposure to potential and recognised toxicants. However, pesticides inventories are often sparse over time and by region, mostly report aggregated classes of active ingredients, and are generally fragmented across local or government authorities, thus hampering an integrated global analysis of pesticide risk. Here, we introduce PEST-CHEMGRIDS, a comprehensive database of the 20 most used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 arc-min resolution (about 10km at the equator) projected from 2015 to 2025. To estimate the global application rates of specifc active ingredients we use spatial statistical methods to re-analyse the USGS/PNSP and FAOSTAT pesticide databases along with other public inventories including global gridded data of soil physical properties, hydroclimatic variables, agricultural quantities, and socio-economic indices. PEST-CHEMGRIDS can be used in global environmental modelling, assessment of agrichemical contamination, and risk analysis.

Legitimacy deficits have been identified as central to the ongoing challenges encountered in implementing the policy reforms introduced to reduce the environmental impacts of over-allocating water in the Murray-Darling Basin. In closing the special issue on Building and Maintaining Trust and Legitimacy in Environmental Water Management, this article draws on the preceding articles in responding to a call for the focus of evaluations of environmental water reforms to be broadened to assess their performance against metrics of legitimacy. The first aim is to consider some analytical issues to be encountered in developing legitimacy metrics for MDB environmental water reform contexts. The other aim is to explore the role of legitimacy metrics in empirical research designed to strengthen the evidence available for deciding whether and how to invest in establishing and sustaining the legitimacy of the MDB reforms. Particular reference is made to empirical studies of the consequences and antecedents of legitimacy in U.S. contexts of the law and its policing. Furnishing policy makers with reliable evidence to guide their decisions on whether and how to invest in the legitimacy of the MDB environmental water reforms will require studies of this kind that are adapted to the unique contexts of these reforms.

Feed formulation using apparent metabolizable energy (AME) corrected to zero nitrogen retention (AMEn) is widely used by poultry nutritionists. Most available tabulated data are from experiments using adult cockerels or growing broilers. Specific values are rarely available for laying hens. A study was conducted to evaluate AME, AMEn, and AMEs (AME adjusted to 50% nitrogen retention) of corn, soybean meal (SBM) and wheat in laying hens using the reference diet substitution and regression methods. Forty eight 42-wk-old Hy-Line Brown hens were used, 2 birds per cage with six replicates per diet. Test diets contained 30% test ingredient (as is basis) and 65.7% reference diet (as is basis) with limestone, other minerals, vitamins, and amino acids held constant across the reference and test diets. Using the reference diet substitution method, AME values obtained for corn, SBM, and wheat were 3,791, 2,621, and 3,565kcal/kg (DM), respectively. The corresponding AMEn values were 3,722, 2,496, and 3,479kcal/kg (DM), and AMEs were 3,784, 2,835, and 3,562kcal/kg (DM), respectively. Calculation of AME, AMEn, and AMEs of ingredients using regression based on the inclusion rate (DM) of dietary ingredients and reference diet gave identical values to those obtained by the reference diet substitution method. In addition, the measured AMEn values of ingredients using laying hens in this study were close to those calculated from proximate composition using the European Union prediction equation based on adult cockerels.

Leucaena has been fed to cattle by the Balinese community in Sumbawa and West Sumbawa districts on Sumbawa Island since the 1980s. However, prior to 2011, this practice was not adopted by the local Sumbawanese farmers. Since then, a model leucaena-based cattle fattening system was developed in Sumbawa and West Sumbawa districts in a collaborative research project between the Assessment Institute for Agricultural Technology (BPTP), University of Mataram and The University of Queensland (UQ) funded by the Australian Centre for International Agricultural Research (ACIAR), followed by a scaling-out project involving collaboration between the University of Mataram and CSIRO (Applied Research and Innovation Systems in Agriculture - ARISA project) funded by DFAT (Department of Foreign Affairs and Trade) promoting public-private partnerships. Further promotion of leucaena-based fattening systems occurred in Dompu, Sumbawa, through a project with the University of Mataram and Massey University funded by the New Zealand Ministry of Foreign Affairs and Trade (MFAT). By the end of October 2018, more than 2,500 farmers on Sumbawa Island were practicing leucaena-based cattle fattening. The main drivers of adoption of cattle fattening with leucaena were: (1) The high growth rates achieved (0.4-0.6 kg/d for bulls fed 100% leucaena and 0.66 kg/d when maize grain was added to the leucaena basal diet) compared with 0.16 kg/d for the traditional system, combined with high profitability; (2) the needs of farmers being met in terms of relevance and cultural appropriateness; (3) field extension staff being well trained and mentored, and respected by the farmers; (4) the local government being highly supportive of leucaena-based cattle fattening; and (5) additional benefits being increased dressing percentage and high carcass quality. The rapid increase in the use of leucaena for cattle fattening in eastern Indonesia is expected to have a significant positive impact on household incomes as well as on regional economic growth.

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image ...

Research and government agencies in eastern Indonesia have identified 2 systems with potential to increase productivity and incomes of small-holder cattle producers: improved cattle feeding practices through forage tree legumes (FTL); and the development of more efficient and specialized cattle-fattening systems. Extensive research has been conducted on production and technical aspects of FTL-fattening systems, but there is a gap in research on economic incentives for households to adopt the systems. This paper provides an economic analysis of a leucaena-fattening system in a village in West Timor. It draws on trial data from associated technical research projects and detailed semi-structured interviews with farmers and other stakeholders to populate a bio-economic model built for the research. Under all measures of profitability, leucaena-fattening systems in representative households are profitable in the wet season. Importantly, 'returns to person days' are higher than off-farm incomes. The activity generates cash income, increasingly required to meet cash expenses in modern rural Indonesian society. However, returns vary considerably between households, are considerably lower in the dry season and, as would be expected, are sensitive to relative prices of feeder and finished cattle.

Australian communities face increasing losses and disruption from natural disasters. Disaster resilience is a protective characteristic that acts to reduce the effects of, and losses from, natural hazard events. Disaster resilience arises from the capacities of social, economic and government systems to prepare for, respond to and recover from a natural hazard event, and to learn, adapt and transform in anticipation of future natural hazard events. This assessment of disaster resilience estimates the status of these capacities and shows how they are spatially distributed across Australia. Composite indices are frequently used to summarize and report complex relational measurements about a particular issue. The Australian Natural Disaster Resilience Index measures disaster resilience as a set of coping and adaptive capacities. Coping capacity is the means by which available resources and abilities can be used to face adverse consequences that could lead to a disaster. Adaptive capacity is the arrangements and processes that enable adjustment through learning, adaptation and transformation. Eight themes of disaster resilience encapsulate the resources and abilities that communities have to prepare for, absorb and recover from natural hazards (social character, economic capital, emergency services, planning and the built environment, community capital, information access) or to adapt, learn and solve problems (social and community engagement, governance and leadership). Across the eight themes, 77 indicators were used to compute the Australian Natural Disaster Resilience Index in 2084 areas of Australia, corresponding to the Statistical Area Level 2 divisions of the Australian Bureau of Statistics. The index was then used to undertake the first nationally standardised assessment of the state of disaster resilience in Australia. Disaster resilience is reported at three levels: an overall disaster resilience index, coping and adaptive capacity sub-indexes and themes of disaster resilience that encapsulate the resources and abilities that communities have to prepare for, absorb and recover from natural hazards and to adapt, learn and solve problems (social character, economic capital, emergency services, planning and the built environment, community capital, information access, social and community engagement, governance and leadership). Volume I (this volume) assesses the state of disaster resilience in Australia, using the Australian Natural Disaster Resilience Index. Volume I gives a brief overview of the design and computation of the index, then assesses the state of disaster resilience in Australia at different levels: overall disaster resilience, coping and adaptive capacity, and the eight themes of disaster resilience. Volume I also presents a typology of disaster resilience that groups areas across Australia that have similar disaster resilience profiles. Readers interested in the results of the assessment of disaster resilience in Australia should focus on Volume I.

Australian communities face increasing losses and disruption from natural disasters. Disaster resilience is a protective characteristic that acts to reduce the effects of, and losses from, natural hazard events. Disaster resilience arises from the capacities of social, economic and government systems to prepare for, respond to and recover from a natural hazard event, and to learn, adapt and transform in anticipation of future natural hazard events. This assessment of disaster resilience estimates the status of these capacities and shows how they are spatially distributed across Australia. Composite indices are frequently used to summarize and report complex relational measurements about a particular issue. The Australian Natural Disaster Resilience Index measures disaster resilience as a set of coping and adaptive capacities. Coping capacity is the means by which available resources and abilities can be used to face adverse consequences that could lead to a disaster. Adaptive capacity is the arrangements and processes that enable adjustment through learning, adaptation and transformation. Eight themes of disaster resilience encapsulate the resources and abilities that communities have to prepare for, absorb and recover from natural hazards (social character, economic capital, emergency services, planning and the built environment, community capital, information access) or to adapt, learn and solve problems (social and community engagement, governance and leadership). Across the eight themes, 77 indicators were used to compute the Australian Natural Disaster Resilience Index in 2084 areas of Australia, corresponding to the Statistical Area Level 2 divisions of the Australian Bureau of Statistics. The index was then used to undertake the first nationally standardised assessment of the state of disaster resilience in Australia. Disaster resilience is reported at three levels: an overall disaster resilience index, coping and adaptive capacity sub-indexes and themes of disaster resilience that encapsulate the resources and abilities that communities have to prepare for, absorb and recover from natural hazards and to adapt, learn and solve problems (social character, economic capital, emergency services, planning and the built environment, community capital, information access, social and community engagement, governance and leadership). Volume II (this volume) describes in detail the computation of the Australian Natural Disaster Resilience Index. This includes resilience concepts, literature review, index structure, data collection, indicators, statistical methods, detailed statistical outputs, sensitivity analysis and uncertainty analyses. Readers interested in the technical aspects of the Australian Natural Disaster Resilience Index should also consider Volume II. Volume II is comprised of six chapters: Chapter 1: Design of the Australian Natural Disaster Resilience Index Chapter 2: Indicators Chapter 3: Computation of the Australian Natural Disaster Resilience Index Chapter 4: Statistical outputs: ANDRI, coping capacity and adaptive capacity Chapter 5: Statistical outputs: disaster resilience themes Chapter 6: Uncertainty and sensitivity analysis

Natural hazards, such as bushfires, cyclones, floods, storms, heatwaves, earthquakes and tsunamis, have always occurred and will continue to occur in Australia. These natural hazards frequently intersect with human societies to create natural hazard emergencies that, in turn, cause disasters.The effects of natural hazards on Australian communities are influenced by a unique combination of social, economic, natural environment, built environment, governance and geographical factors.Australian communities face increasing losses and disruption from natural hazards, with the total economic cost of natural hazards in Australia averaging $18.2 billion per year between 2006 and 2016 (Deloitte Access Economics, 2017). This is expected to almost double by 2030 and to average $33 billion per year by 2050 (Deloitte Access Economics, 2016). The social impacts of disasters are also substantial. Costs associated with social impacts may persist over a person's lifetime and can be greater than the costs of tangible damages (Deloitte Access Economics, 2016).Climate change is expected to increase the frequency and magnitude of some natural hazard types in Australia (BOM & CSIRO, 2018). An increasing population, demographic change, widening socio-economic disparity, expensive infrastructure and the location of communities in areas of high natural hazard risk also contributes to the potential for increasing losses from natural hazards.There are two prominent schools of thought about the influence of natural hazards in human societies:a vulnerability perspective, where distributional inequalities in physical, social, economic and environmental factors influence the susceptibility of people to harm and the ability of people to respond to hazards (Cutter et al., 2003; Birkmann, 2006; Bankoff, 2019).a resilience perspective, where people are learning to live with a changing, unpredictable and uncertain environment (Folke et al., 2002; Bankoff, 2019), of which natural hazards are a part. Resilience is a process linking a set of capacities to a positive trajectory of functioning and adaptation after a disturbance (Norris et al., 2008).This resilience perspective has been adopted in the Australian Disaster Resilience Index, with the aim of better understanding and assessing the disaster resilience of Australian communities nationwide.As such, disaster resilience can be understood as a protective characteristic that acts to reduce the effects of, and losses from, natural hazards. Resilience arises from the capacities of social, economic and government systems to prepare for, respond to and recover from a natural hazard event, and to learn, adapt and transform in anticipation of future natural hazard events.