Examining the relationship between farm size and productive efficiency: a Bayesian directional distance function approach

Achieving sustainable food security and increased farm income will depend on how efficient production systems are in converting available inputs to produce outputs. Using data from Malawi, we estimate a Bayesian directional technology distance function to examine the relationship between farm size and technical efficiency. Our results support the existence of an inverse relationship between farm size and productive efficiency, where small farms are more efficient than large farms. On average, farms exhibit inefficiency levels of 60%, suggesting that productivity could be improved substantially. Improving productive efficiency and food security will require farms to operate in ways where the size of cultivated area is matched by nonland production inputs such as labor, fertilizer, and improved seeds. The results highlight the need for policies that could incentivize farmers to adopt productivity‐enhancing technologies and, where possible, to allocate excess land to lease markets.     

Complements, Substitutes, Budget Constraints and Valuation

This study employs a multi-program contingent valuation (CVM) design tosimultaneously assess the value of three ecosystem conservation programsin Alberta, Canada. The design is different from most other CVM designsand has several different features including the natural incorporation ofdirect reminders of substitute/complementary programs and budgetconstraints. In contrast to the findings of other studies, two of the environmentalprograms appear to be complements and other combinations of the programssuggest an absence of substitution effects. The multi-program model ismore informative and robust in terms of theoretical validity and expectedrelationships with demographic and recreational characteristics of therespondents.     

Estimating shadow price for symbiotic nitrogen and technical efficiency for legume‐based conservation agriculture in Malawi

Determining the value of legumes as soil fertility amendments can be challenging, yet this information is required to guide public policy and to incentivise prescribed land‐management practices such as conservation agriculture. We use a directional input distance function (DIDF) to estimate shadow prices for symbiotic nitrogen and the technical efficiency for mixed maize‐legume production systems in Malawi. The shadow prices reflect the trade‐off between fertiliser nitrogen and symbiotic nitrogen required to achieve a given quantity of output. Our results reveal considerable technical inefficiency in the production system. The estimated shadow prices vary across farms and are, on average, higher than the reference price for commercial nitrogen. The results suggest that it would be beneficial to redesign the current price‐support programs that subsidise chemical fertilisers and indirectly crowd‐out organic soil amendments such as legumes.     

Choice of Rice Production Practices in Ghana: A Comparison of Willingness to Pay and Preference Space Estimates

Rice has been identified as an important food security crop in Ghana. However, there is a production deficit and new technologies to reduce the deficit are not widely adopted. Although poor adoption by farmers’ is often linked to constraints such as access to information, farmers’ perceptions of the technologies are also important. We apply an advanced discrete choice experiment to evaluate farmers’ preferences for rice production practices. Specifically, we generate willingness to pay (WTP) estimates using willingness to pay space (WS) and compare these with values from the indirect or preference space (PS) method. Our modelling also accounts for the effects on WTP estimates of farmers’ stated attribute importance (SAI) information. Empirical results from WS and PS models reveal that on average, farmers value higher yields and are negatively affected by higher risk of crop failure and labour requirements. Comparing the performance of the two models, we find the WS model provides a superior fit to our data and reduces the likelihood of producing implausible WTP estimates. Further, SAI inclusion did not produce much variation in our WTP estimates.     

Testing for allocation efficiencies in water quality tenders across catchments,industries and pollutants: a north Queensland case study*

The design of competitive tenders to purchase environmental services requires judgements to be made about the funding scale and tender scope, with the latter incorporating considerations of geographic area, industries involved and the types of environmental outputs required. Increasing the scale and scope of tenders increases the likelihood that a larger range of proposals will be proposed and cost‐effective ones selected. However, the use of larger and more broadly scoped tenders may reduce landholder participation and increase asking bids. In the study reported here, these issues have been tested with a single water quality tender run in north‐eastern Australia in 2007 and 2008. Post hoc tests and workshop exercises show that while larger‐scale and scope tenders can generate efficiency gains, care has to be taken to maintain participation and avoid higher bid levels.     

A Luenberger soil-quality indicator

This paper discusses how a quality indicator can be constructed as a Luenberger indicator using directional distance functions. This leads to a suggested formula for aggregating individual soil quality attributes into an indicator. The method is illustrated using crop trials data from Western Australia. The approach presented is theoretically consistent and may provide a better alternative to many of the ad hoc procedures used to construct soil-quality indexes in the soil science literature. Our approach also addresses shortcomings related to quality indicators previously proposed in the productivity literature using Malmquist indices.     

Event‐specific data envelopment models and efficiency analysis*

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state‐contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event‐specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event‐specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.     

Sustainable agricultural intensification practices and cost efficiency in smallholder maize farms: Evidence from Ethiopia

Sustainable agricultural intensification (SAI) practices have been promoted in developing countries to improve farm productivity and environmental services. However, it is still unclear whether use of a combination of SAI practices has a significant influence on the cost efficiency of smallholder farms. This study investigates whether the cost efficiency of smallholder maize producers in Ethiopia depends on use of input-augmenting and agronomic SAI practices. Stochastic cost frontier models that account for heterogeneity across farms are used for estimation. We find that the use of individual SAI practices increases cost while the combined use of the practices reduces it. The marginal effect on cost reduction is substantial when SAI practices are set at their sample maximum levels. Overall, the results demonstrate the benefit of using a combination of SAI practices in minimizing production cost while addressing the adverse effects of soil degradation and climate variability. To enhance food security, smallholder farm incomes and the sustainability of production in developing countries, policy programs should focus on promoting packages of input-augmenting and agronomic SAI practices.