Using Our Bodies Faithfully : Christian Friendship and the Life of Worship

Peer reviewedPostprin

CAPTURING HOUSEHOLD-LEVEL SPATIAL INFLUENCE IN AGRICULTURAL MANAGEMENT USING RANDOM EFFECTS REGRESSION

Data on agricultural and natural resource management typically have spatial patterns related to the landscapes from which they came. Consequently, econometric models designed to explain the determinants of humans' natural resource management practices or their outcomes often have spatial structure that can bring bias or inefficiency to parameter estimates. Although econometric tools are available to correct for spatial structure, such tools are largely lacking for use with discrete dependent variable models. While one obvious solution would be to develop the necessary tools, an alternative is to identify conditions under which spatial dependency can be managed effectively without formal spatial autoregressive models. This study examines conditions under which spatial structure corresponds closely to defined agro-ecological zones, making it possible to model spatial effects by random effects regression. Using household survey data sampled along agro-ecological zone strata, this article develops two models of links between farmer assets and agricultural natural resource degradation in southern Peru. The first stage model looks at determinants of crop yield loss over time (an index of soil productivity), while the second stage model looks at determinants of the extent of fallow cycles in crop rotation, a key agricultural practice reducing crop yield loss. Diagnostic statistics for spatial dependency reveal spatial structure, particularly in the fallow model. This spatial dependency is eliminated in the ordinary least squares (OLS) models by inclusion of the agro-ecological zone random effects. In the spatially dependent fallow model, comparison of coefficient estimates between OLS and the spatial autoregressive maximum likelihood models showed OLS with random effects to give virtually identical results to the spatial autoregressive models, making the latter unnecessary. These results show that spatial structure in natural resource management models can sometimes be captured by zonal variables. When this occurs, random effects regression can largely eliminate spatial dependency. A necessary precondition for this approach with household survey data is prior sample stratification according to landscape characteristics. Where random effects models can effectively capture spatial structure, they may also offer analysts greater flexibility in analyzing models with limited dependent variables.Farm Management,

MORE SOCIAL CAPITAL, LESS EROSION: EVIDENCE FROM PERU'S ALTIPLANO

The debate over sustainable intensification has hinged on private incentives to abate land degradation. Largely missing is the role of social capital in both creating incentives and removing barriers to soil conservation. Yet soil conservation embodies the externality problem that bedevils so many aspects of natural resource management. Action by one farmer to reduce water or wind erosion may benefit neighboring fields by slowing the rate of water or wind movement across those lands. Yet these benefits are not fully captured by the farmer making the conservation investment. However, when economic agents care for one another, these externalities can be internalized, reducing the individual's disincentive to perform a socially level of natural resource conservation. Likewise, community organizations may provide collective capital and labor to overcome adoption barriers faced by individuals. The twin hypotheses that 1) farming practices influence soil erosion and 2) social capital influences the adoption of sustainable farming practices are tested with data from a 1999 survey of 197 farms in the Peruvian Altiplano around Lake Titicaca. The survey used cluster sampling of farms in villages to represent each of three arable agro-ecological zones in the Ilave-Huenque river basin. Relative asset levels were used to stratify resident households within villages. Personal interviews collected a wide range of data on farm household assets, management practices, and status of agricultural natural resources.International Relations/Trade, Land Economics/Use,

Reimagining Farms as Managed Ecosystems

Ecosystem Services, Managed Ecosystems, Payment for Environmental Services, Multidisciplinary Research, Nonmarket Valuation, Environmental Economics and Policy, Resource /Energy Economics and Policy, Q57, Q51, Q12,

TOO POOR TO BE STEWARDS? RURAL POVERTY AND SUSTAINABLE NATURAL RESOURCE MANAGEMENT

Sustaining natural resource stocks especially those underpinning the capacity to produce food is key to most definitions of sustainable development. Yet troubling evidence has surfaced of instances where the rural poor were forced to sacrifice long-term sustainability for the sake of near-term survival (Mink 1993; Figueroa 1998). Are such cases special ones, or is rural poverty a driving factor in causing soil erosion, overgrazing, deforestation, and degradation of other natural resources? This paper argues that natural resource sustainability in developing countries is not the result of a direct cause-effect relationship, but rather is engendered by a web of causal factors. Untangling that web entails separating out strands for poverty from those for location-specific natural resource conditions, human institutions, technology, and population. This paper reviews the history of the poverty-environment debate, examines three sets of case studies that shed light on key relationships, and finally proposes policy interventions to promote the sustainability of the natural resources that underpin agricultural productivity.Food Security and Poverty, Resource /Energy Economics and Policy,

As Ecosystem Services Are Demanded of Agriculture, What of Agricultural Economists?

Environmental Economics and Policy, Teaching/Communication/Extension/Profession,

ESTIMATING COST EFFICIENCY AMONG MAIZE PRODUCERS IN KENYA AND UGANDA

A translog stochastic cost frontier is estimated to calculate cost efficiency index (CEI) of 203 Kenyan and Ugandan maize producers using survey data from 2003. Results show a mean CEI of 1.95, with greater inefficiency among farms in Kenya of medium-large size. Key sources of inefficiency are late planting and use of recycled seed.Farm Management,

What GPs mean by 'spirituality' and how they apply this concept with patients : a qualitative study

This study was funded through a small bursary from the Centre for Rural Health.Peer reviewedPublisher PD

Market Interactions, Farmer Choices, and the Sustainability of Growing Advanced Biofuels

Advanced biofuels such as cellulosic ethanol are of great interest for their potential to supply a significant portion of U.S. fuel needs plus advantages over corn grain-based ethanol. The sustainability of agriculture-based advanced biofuels depends on how farmers would respond in providing biomass feedstock, yet economic behavior by farmers has been under recognized by the science community. Focusing on markets and policy incentives, this research shows that farmers are unlikely to convert current grain cropland to grow a dedicated cellulosic biomass crop such as switchgrass. However, the financial incentives to harvest cellulosic biomass provided by the 2008 farm bill may stimulate corn production due to demand for corn grain for feed and ethanol and corn residues for advanced biofuels. The prospect of continuous, possibly expanding corn production for advanced biofuels raises the same environmental issues as for corn grain-based ethanol. To assure the environmental sustainability of advanced biofuels production, environmental policies are needed to complement existing bioenergy initiatives.biomass, energy, advanced biofuels, corn, land use, switchgrass, cellulosic ethanol, Environmental Economics and Policy, Production Economics, Resource /Energy Economics and Policy, Q42, Q12,

ECONOMICS IN THE DESIGN, ASSESSMENT, ADOPTION, AND POLICY ANALYSIS OF I.P.M.

During the past twenty years, economics has played a key role in technology assessment and policy analysis related to integrated pest management (IPM) practices. The paper reviews economic analysis of IPM as applied to evaluating expected profitability, ex ante and ex post adoption, social welfare impacts, returns to research, and policies that affect pest management generally. In specific cases, economic methods have contributed significantly to the development of threshold-based IPM decision support software. Two areas that need greater economic input are assessment of biological pest management practices and the measurement of returns to research in IPM.Crop Production/Industries,