Australian Bureau of Agricultural and Resource Economics

Experimental economists are leaving the reservation. They are recruiting subjects in the field rather than in the classroom, using field goods rather than induced valuations, and using field context rather than abstract terminology in instructions. We argue that there is something methodologically fundamental behind this trend. Field experiments differ from laboratory experiments in many ways. Although it is tempting to view field experiments as simply less controlled variants of laboratory experiments, we argue that to do so would be to seriously mischaracterize them. What passes for “control” in laboratory experiments might in fact be precisely the opposite if it is artificial to the subject or context of the task. We propose six factors that can be used to determine the field context of an experiment: the nature of the subject pool, the nature of the information that the subjects bring to the task, the nature of the commodity, the nature of the task or trading rules applied, the nature of the stakes, and the environment that subjects operate in.

Herbicides are the foundation of weed control in commercial crop-production systems. However, herbicide-resistant (HR) weed populations are evolving rapidly as a natural response to selection pressure imposed by modern agricultural management activities. Mitigating the evolution of herbicide resistance depends on reducing selection through diversification of weed control techniques, minimizing the spread of resistance genes and genotypes via pollen or propagule dispersal, and eliminating additions of weed seed to the soil seedbank. Effective deployment of such a multifaceted approach will require shifting from the current concept of basing weed management on single-year economic thresholds.

Wild and managed bees are well documented as effective pollinators of global crops of economic importance. However, the contributions by pollinators other than bees have been little explored despite their potential to contribute to crop production and stability in the face of environmental change. Non-bee pollinators include flies, beetles, moths, butterflies, wasps, ants, birds, and bats, among others. Here we focus on non-bee insects and synthesize 39 field studies from five continents that directly measured the crop pollination services provided by non-bees, honey bees, and other bees to compare the relative contributions of these taxa. Non-bees performed 25-50% of the total number of flower visits. Although non-bees were less effective pollinators than bees per flower visit, they made more visits; thus these two factors compensated for each other, resulting in pollination services rendered by non-bees that were similar to those provided by bees. In the subset of studies that measured fruit set, fruit set increased with non-bee insect visits independently of bee visitation rates, indicating that non-bee insects provide a unique benefit that is not provided by bees. We also show that non-bee insects are not as reliant as bees on the presence of remnant natural or seminatural habitat in the surrounding landscape. These results strongly suggest that non-bee insect pollinators play a significant role in global crop production and respond differently than bees to landscape structure, probably making their crop pollination services more robust to changes in land use. Non-bee insects provide a valuable service and provide potential insurance against bee population declines.

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Feeding 9-10 billion people by 2050 and preventing dangerous climate change are two of the greatest challenges facing humanity. Both challenges must be met while reducing the impact of land management on ecosystem services that deliver vital goods and services, and support human health and well-being. Few studies to date have considered the interactions between these challenges. In this study we briefly outline the challenges, review the supply- and demand-side climate mitigation potential available in the Agriculture, Forestry and Other Land Use AFOLU sector and options for delivering food security. We briefly outline some of the synergies and trade-offs afforded by mitigation practices, before presenting an assessment of the mitigation potential possible in the AFOLU sector under possible future scenarios in which demand-side measures codeliver to aid food security. We conclude that while supply-side mitigation measures, such as changes in land management, might either enhance or negatively impact food security, demand-side mitigation measures, such as reduced waste or demand for livestock products, should benefit both food security and greenhouse gas (GHG) mitigation. Demand-side measures offer a greater potential (1.5-15.6 Gt CO2 -eq. yr(-1) ) in meeting both challenges than do supply-side measures (1.5-4.3 Gt CO2 -eq. yr(-1) at carbon prices between 20 and 100 US$ tCO2 -eq. yr(-1) ), but given the enormity of challenges, all options need to be considered. Supply-side measures should be implemented immediately, focussing on those that allow the production of more agricultural product per unit of input. For demand-side measures, given the difficulties in their implementation and lag in their effectiveness, policy should be introduced quickly, and should aim to codeliver to other policy agenda, such as improving environmental quality or improving dietary health. These problems facing humanity in the 21st Century are extremely challenging, and policy that addresses multiple objectives is required now more than ever.

Understanding the capacity of agricultural systems to feed the world population under climate change requires projecting future food demand. This article reviews demand modeling approaches from 10 global economic models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP). We compare food demand projections in 2050 for various regions and agricultural products under harmonized scenarios of socioeconomic development, climate change, and bioenergy expansion. In the reference scenario (SSP2), food demand increases by 59-98% between 2005 and 2050, slightly higher than the most recent FAO projection of 54% from 2005/2007. The range of results is large, in particular for animal calories (between 61% and 144%), caused by differences in demand systems specifications, and in income and price elasticities. The results are more sensitive to socioeconomic assumptions than to climate change or bioenergy scenarios. When considering a world with higher population and lower economic growth (SSP3), consumption per capita drops on average by 9% for crops and 18% for livestock. The maximum effect of climate change on calorie availability is -6% at the global level, and the effect of biofuel production on calorie availability is even smaller.

Using a model based on a trade‐off between moral hazard incentives and gains from specialization, this paper explains why farming has generally not converted from small, family‐based firms into large, factory‐style corporate firms. Nature is both seasonal and random, and the interplay of these qualities generates moral hazard, limits the gains from specialization, and causes timing problems between stages of production. By identifying conditions in which these forces vary, we derive testable predictions about the choice of organization and the extent of farm integration. To test these predictions we study the historical development of several agricultural industries and analyze data from a sample of over 1,000 farms in British Columbia and Louisiana. In general, seasonality and randomness so limit the benefits of specialization that family farms are optimal, but when farmers are successful in mitigating the effects of seasonality and random shocks to output, farm organizations gravitate toward factory processes and corporate ownership.

The parameter values and assumptions of any economic model are subject to change and error. Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model. There is a very large literature on procedures and techniques for SA, but it includes almost nothing from economists. This paper is a selective review and overview of theoretical and methodological issues in SA. There are many possible uses of SA, described here within the categories of decision support, communication, increased understanding or quantification of the system, and model development. The paper focuses somewhat on decision support. It is argued that even the simplest approaches to SA can be theoretically respectable in decision support if they are applied and interpreted in a way consistent with Bayesian decision theory. This is not to say that SA results should be formally subjected to a Bayesian decision analysis, but that an understanding of Bayesian probability revision will help the modeller plan and interpret an SA. Many different approaches to SA are described, varying in the experimental design used and in the way results are processed. Possible overall strategies for conducting SA are suggested. It is proposed that when using SA for decision support, it can be very helpful to attempt to identify which of the following forms of recommendation is most appropriate: (a) do X, (b) do either X or Y depending on the circumstances, (c) do either X or Y, whichever you like, (d) if in doubt, do X. A system for reporting and discussing SA results is recommended. (C) 1997 Elsevier Science B.V.

A "supermarket revolution" has occurred in developing countries in the past 2 decades. We focus on three specific issues that reflect the impact of this revolution, particularly in Asia: continuity in transformation, innovation in transformation, and unique development strategies. First, the record shows that the rapid growth observed in the early 2000s in China, Indonesia, Malaysia, and Thailand has continued, and the "newcomers"--India and Vietnam--have grown even faster. Although foreign direct investment has been important, the roles of domestic conglomerates and even state investment have been significant and unique. Second, Asia's supermarket revolution has exhibited unique pathways of retail diffusion and procurement system change. There has been "precocious" penetration of rural towns by rural supermarkets and rural business hubs, emergence of penetration of fresh produce retail that took much longer to initiate in other regions, and emergence of Asian retail developing-country multinational chains. In procurement, a symbiosis between modern retail and the emerging and consolidating modern food processing and logistics sectors has arisen. Third, several approaches are being tried to link small farmers to supermarkets. Some are unique to Asia, for example assembling into a "hub" or "platform" or "park" the various companies and services that link farmers to modern markets. Other approaches relatively new to Asia are found elsewhere, especially in Latin America, including "bringing modern markets to farmers" by establishing collection centers and multipronged collection cum service provision arrangements, and forming market cooperatives and farmer companies to help small farmers access supermarkets.

Changes in agricultural land use have important implications for environmental services. Previous studies of agricultural land-use futures have been published indicating large uncertainty due to different model assumptions and methodologies. In this article we present a first comprehensive comparison of global agro-economic models that have harmonized drivers of population, GDP, and biophysical yields. The comparison allows us to ask two research questions: (1) How much cropland will be used under different socioeconomic and climate change scenarios? (2) How can differences in model results be explained? The comparison includes four partial and six general equilibrium models that differ in how they model land supply and amount of potentially available land. We analyze results of two different socioeconomic scenarios and three climate scenarios (one with constant climate). Most models (7 out of 10) project an increase of cropland of 10–25% by 2050 compared to 2005 (under constant climate), but one model projects a decrease. Pasture land expands in some models, which increase the treat on natural vegetation further. Across all models most of the cropland expansion takes place in South America and sub-Saharan Africa. In general, the strongest differences in model results are related to differences in the costs of land expansion, the endogenous productivity responses, and the assumptions about potential cropland.

Abstract The 1998 global coral bleaching event was the largest recorded historical disturbance of coral reefs and resulted in extensive habitat loss. Annual censuses of reef fish community structure over a 12‐year period spanning the bleaching event revealed a marked phase shift from a prebleach to postbleach assemblage. Surprisingly, we found that the bleaching event had no detectable effect on the abundance, diversity or species richness of a local cryptobenthic reef fish community. Furthermore, there is no evidence of regeneration even after 5–35 generations of these short‐lived species. These results have significant implications for our understanding of the response of coral reef ecosystems to global warming and highlight the importance of selecting appropriate criteria for evaluating reef resilience.

Australia is among the most fire-prone of continents. While national fire management policy is focused on irregular and comparatively smaller fires in densely settled southern Australia, this comprehensive assessment of continental-scale fire patterning (1997–2005) derived from ~1 km2 Advanced Very High Resolution Radiometer (AVHRR) imagery shows that fire activity occurs predominantly in the savanna landscapes of monsoonal northern Australia. Statistical models that relate the distribution of large fires to a variety of biophysical variables show that, at the continental scale, rainfall seasonality substantially explains fire patterning. Modelling results, together with data concerning seasonal lightning incidence, implicate the importance of anthropogenic ignition sources, especially in the northern wet–dry tropics and arid Australia, for a substantial component of recurrent fire extent. Contemporary patterns differ markedly from those under Aboriginal occupancy, are causing significant impacts on biodiversity, and, under current patterns of human population distribution, land use, national policy and climate change scenarios, are likely to prevail, if not intensify, for decades to come. Implications of greenhouse gas emissions from savanna burning, especially seasonal emissions of CO2, are poorly understood and contribute to important underestimation of the significance of savanna emissions both in Australian and probably in international greenhouse gas inventories. A significant challenge for Australia is to address annual fire extent in fire-prone Australian savannas.

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.

This review by a multidisciplinary team maps key components and emerging connections within the intellectual landscape of agroecology. We attempt to extend and preview agroecology as a discipline in which agriculture can be conceptualized within the context of global change and studied as a coupled system involving a wide range of social and natural processes. This intrinsic coupling, combined with powerful emerging drivers of change, presents challenges for the practice of agroecology and agriculture itself, as well as providing the framework for some of the most innovative research areas and the greatest potential for innovation for a sustainable future in agriculture. The objective of this review is to identify forward-looking scientific questions to enhance the relevance of agroecology for the key challenges of mitigating environmental impacts of agriculture while dramatically increasing global food production, improving livelihoods, and thereby reducing chronic hunger and malnutrition over the coming decades.

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of “real world commodity prices” differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between −0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales.

A simple method for estimating population distribution functions and associated quantiles from sample survey data is described and some asymptotic theory for it presented. The method assumes a model-based approach to survey estimation and allows auxiliary population information to be directly incorporated into the estimation process. Monte Carlo results comparing the proposed method with conventional design-based methods are given. These suggest that the model-based approach offers significant gains when the auxiliary population information is linearly related to the survey variables of interest.

The traditional reductionist approach to science has a tendency to create 'islands of knowledge in a sea of ignorance', with a much stronger focus on analysis of scientific inputs rather than synthesis of socially relevant outcomes. This might be the principal reason why intended end users of climate information generally fail to embrace what the climate science community has to offer. The translation of climate information into real-life action requires 3 essential components: salience (the perceived relevance of the information), credibility (the perceived technical quality of the information) and legitimacy (the perceived objectivity of the process by which the information is shared). We explore each of these components using 3 case studies focused on dryland cropping in Australia, India and Brazil. In regards to 'salience' we discuss the challenge for climate science to be 'policy-relevant', using Australian drought policy as an example. In a village in southern India 'credibility' was gained through engagement between scientists and risk managers with the aim of building social capital, achieved only at high cost to science institutions. Finally, in Brazil we found that 'legitimacy' is a fragile, yet renewable resource that needs to be part of the package for successful climate applications; legitimacy can be easily eroded but is difficult to recover. We conclude that climate risk management requires holistic solutions derived from cross-disciplinary and participatory, user-oriented research. Approaches that combine climate, agroecological and socioeconomic models provide the scientific capabilities for establishment of 'borderless' institutions without disciplinary constraints. Such institutions could provide the necessary support and flexibility to deliver the social benefits of climate science across diverse contexts. Our case studies show that this type of solution is already being applied, and suggest that the climate science community attempt to address existing institutional constraints, which still impede climate risk management.

\n \t\t\tWe assess a replicated fire plot experiment undertaken between 1973 and 1996 in two Eucalyptus-dominated savanna vegetation formations (open forest, woodland), at Munmarlary, in monsoonal northern Australia. Four treatments, each with three replicates, were imposed on each vegetation type: annual early dry-season burning; annual late dry-season burning; biennial early dry-season burning; and unburned controls. Treatments were imposed faithfully, with noted exceptions, on 1-ha plots. Fire intensities were typically low (<1000 kW/m) to moderate (1000-2500 kW/m), varied significantly between treatments, and generally were greater in woodland. In both woodland and open forest, pH was significantly lower and NO3-N was significantly higher in unburned plots. Organic C was not significantly greater. in unburned treatments. Effects of fire regime on other soil chemical properties differed between open forest and woodland sites. Among the grasses, invariant frequent burning led to the dominance of a small number of annual species, notably regionally dominant Sorghum. In the absence of burning, annuals declined generally, whereas some perennials increased while most decreased. These responses usually were apparent within the first five years of the experiment. At the relatively, small spatial scale of the grass sampling regime, there was high turnover of both annual and perennial grasses. Under low- to moderate-intensity, frequent burning regimes, woody vegetation dominated by mature eucalypts is structurally stable. In the absence of burning for at least five years, there was release of the non-eucalypt, woody component into the midstory; this occurred more rapidly in open forest. Accession of rain forest species occurred on some woodland plots, especially the unburned treatment. In contrast, eucalypts were not released significantly from the understory. Rather, as suggested by other studies, recruitment of eucalypts into the canopy appears to involve significantly reduced root competition through death of dominant eucalypts. Although the Munmarlary experiment provides invaluable quantitative data for exploring relationships between fire regimes and the responses of north Australian savanna systems, it has been less successful in meeting the complex information requirements of regional fire managers. Replicated experimental fire plot designs, no matter how elegant and rigorously implemented, may substantially fail the test of management relevance , given the fundamental requirement for savanna biodiversity managers to experience the integrated effects of fire regimes that vary idiosyncratically over multiple time and spatial scales. We suggest that such information requirements are better met through modest, targeted adaptive management studies, involving collaborative partnerships between managers and researchers.\n

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global realtime drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system.

Land fragmentation, where a single farm has a number of parcels of land, is a common feature of agriculture in many countries, especially in developing countries. In Vietnam, land fragmentation is common, especially in the north. For the whole country, there are about 75 million parcels of land, an average of seven to eight plots per farm household. Such fragmentation can be seen to have negative and positive benefits for farm households and the community generally. Comparative statics analysis and analysis of survey data have led to the conclusion that small‐sized farms are likely to be more fragmented, and that fragmentation had a negative impact on crop productivity and increased family labour use and other money expenses. Policies which allow the appropriate opportunity cost of labour to be reflected at the farm level may provide appropriate incentives to trigger farm size change and land consolidation. Policies which tip the benefits in favour of fewer and larger plots, such as strong and effective research and development, an active extension system and strong administrative management, may also lead to land consolidation.