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AbstractThe study was carried out in Murehwa, Mutoko, and Goromonzi district in Mashonaland East Province, Zimbabwe guided by the objective of analyzing factors that determine commercialization of African indigenous vegetables (AIVs) by smallholder farmers with different characterization. Quantitative techniques were used to collect data from three purposively sampled districts and 250 randomly sampled smallholder farmers using a questionnaire. The fractional regression model (FRM) was used to regress the explanatory variables. The paper revealed that the bulk of the households who participated were male-headed, married households and unemployed. Having a bulk of participants as male-headed households has a possible negative effect on commercialization. About 54.8% of households of the sampled population participated in the selling of African indigenous vegetables. Commercialization was significantly affected by the age of the household head, the quantity of AIVs sold, estimated income, employment status and distance to the main market which in this case is Mbare Musika. Mutoko has many households participating in the African indigenous vegetables commercialization. However, their index is low compared to Goromonzi which had few farmers but with a higher commercialization index, reflecting the volume per farmer that reaches the market. Commercialization of AIVs was found to be viable at 60 kg and above of quantities sold to the market. Household income had an inverse relationship with the commercialization index. Capacitating the younger generation in line with benefits of AIVs can enhance their participation in production and marketing and propel commercialization.

Micro and small wood-processing enterprises represent the heart of European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the provision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is used in the current research, which is a nonparametric, linear-programming-based approach that is commonly used to analyse the efficiency of organisational units. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterprises in EU countries and reveal the hidden inputs that facilitate efficiency generation. The economic efficiency evaluation was carried out on the basis of the official statistical data for the micro and small wood-processing companies in EU member states for the period 2015–2020 by performing a two-stage DEA analysis. The data used were standardised by value per employee. In addition to the first stage of DEA, a fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically efficient entities. The only contextual variable affecting the economic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase in the investments. [ABSTRACT FROM AUTHOR]

This paper uses the theoretical underpinnings of the real options theory framework to investigate whether uncertainty affects a firms' decision to directly export versus indirectly export and to determine whether indirect exports may offset the negative effects of uncertainty on export intensity. Using firm-level survey data of 40 emerging countries, we find that uncertainty and firms' export intensity are negatively correlated. We also find that an increase in uncertainty increases a firms' willingness to export through trade intermediaries. These results when considered holistically imply that trade intermediaries are able to countervail the negative effect of uncertainty on firms' export intensity. [ABSTRACT FROM AUTHOR]

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ABSTRACT FROM AUTHOR]

Abstract Background The agricultural economy has little room for emerging farmers and there is no strong support system available for the small-scale farmers venturing in agro-processing. In this study, “access to agro-processing training” refers to any processing training rendered to small-scale crop farmers to equip them when venturing in to agro-processing. Small-scale crop farmers trained with high knowledge in processing and skills are pre-disposed to adopting processing as a strategy of making their processed products penetrate the agro-processing market. Methodology Data were collected from 307 small-scale crop farmers and STATA version 15 was used to perform fractional regression analyses to determine factors influencing access to training from the five types of agro-processing training (marketing training, processing training, record-keeping training, financial management training and business-plan training). Results The results revealed that 26% of the small-scale crop farmers had no access to agro-processing training and 74% of the small-scale crop farmers had access to training. Farming experience had significant influence on the access to agro-processing training for small-scale farmers to function efficiently in the agro-processing industry at 5% level of significance and their coefficient was positive. Conclusion Small-scale crop farmers with less farming experience should be encouraged to participate in the agro-processing sector as their participation can result in improved income and food security at the household levels. Furthermore, new agro-processing training programmes should be encouraged as trained farmers are more likely to participate in the value addition activities of agro-processing.

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production. [ABSTRACT FROM AUTHOR]

We extend the notion of a two-part fractional regression model with conditional free disposal hull efficiency responses to accommodate two-stage regression analysis. The two-part regression model includes the binomial model with a nonlinear specification for the expected response in (0,1] and is a more general formulation in the context of fractional regressions. We use nonlinear least squares to assess the effect of covariates in the conditional efficiency response. The approach is applied to Brazilian agricultural county data, as reported in the Brazilian agricultural census of 2006. The efficiency measure is output oriented and assumes variable returns to scale. Output is rural gross income and inputs are land expenses, labor expenses and expenses on other technological inputs. The covariates affecting production are credit, technical assistance, a rural development index, income concentration, measured by the Gini index, and regional dummies. Overall Brazilian rural production performance responds positively to all covariates. [ABSTRACT FROM AUTHOR]

Research background: Despite the growing social recognition of the positive role played by organic farming in the conservation of natural resources and the reduction or elimination of the negative externalities of modern agriculture, the economic competitiveness of organic versus conventional agriculture is a contentious issue. Studies on scale efficiency in the agricultural economics literature, in general, did not address the differences in production practices such as organic and conventional production. Purpose of the article: We estimated scale efficiency of organic and conventional production, tested for differences between organic and conventional agriculture scale efficiency, and explored the sources of inefficiencies. Methods: This was accomplished using cross-sectional data on 658 organic and conventional cocoa farmers, for the 2012/13 production season in the Eastern Region of Ghana. The analysis accounted for selection bias and recognised the fractional property of the scale efficiency measure. Findings & Value added: Organic agriculture is less scale efficient than conventional agriculture. Whilst we recommend that both producer groups improve scale efficiency, organic producers require greater work to do to make up for the almost 50% scale inefficiency. We also found farmer-based organisations to significantly influence scale efficiency. This calls for the need to strengthen farmer-based organisations to increase participation, among other reasons. We departed from the existing scale efficiency literature in a three of ways. We accounted for selection-bias using propensity score matching in the organic and conventional samples in analysing scale efficiency, modelled scale inefficiency using fractional regression and empirically selected the appropriate link function using a battery of tests. Finally, we accounted for an important policy variable; farmer-based organisation. We employed propensity score matching that accounted from observable biases. Further research may consider other methods that account for both observed and unobserved variations.

This article documents the patterns of White-Black and White-Hispanic enrollment gaps in Advanced Placement (AP) and Dual Enrollment (DE) programs across thousands of school districts in the United States by merging several data sources. We show that the vast majority of districts have racial enrollment gaps in both programs, with wider gaps in AP than DE. Results from fractional regression models indicate that geographic variations in these gaps can be explained by both local and state factors. We also find that district-level resources and state policies that provide greater access to AP and DE are also associated with wider racial enrollment gaps, implying that greater resources may engender racial disparity without adequate efforts to provide equitable access and support for minority students. [ABSTRACT FROM AUTHOR]

Abstract: The study sets out to measure the technical, allocative and cost efficiencies of maize farming in the Northern Region of Ghana for the 2014-2015 farming season. The region has 73% of its population as farmers, with maize being the most cultivated and consumed cereal. Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) are employed in the estimation. Interviews were conducted on 121 farmers selected through a mixed sampling technique. The study also segregated the quantities and prices of nitrogen and phosphorus from compound fertilizers. Under DEA, the study found an average efficiency of 79%, 67% and 53% for technical, allocative and cost efficiencies respectively. For SFA the results respectively are 77%, 27% and 21%. Cost and allocative efficiency estimates were very low especially for SFA. Using fractional regression analysis, it was found that household structure of farmers determined their technical efficiencies. Maize land size, marital status, education, and maize farming experience were found to affect allocative efficiency whiles marital status, household structure and maize farming experience affected cost efficiency. The study also found that labor was excessively used in the production process. Fertilizer application increased maize productivity more than other inputs. Policy recommendations were made following these findings.

BACKGROUND Wildlife damage to crops is a persistent and costly problem for many farmers in the USA. Most existing estimates of crop damage have relied on direct assessment methods such as field studies conducted by trained biologists or surveys distributed to farmers. In this paper, we describe a new method of estimating wildlife damage that exploits federal crop insurance data. We focused our study on four crops: corn, soybean, wheat, and cotton, chosen because of their economic importance and their vulnerability to wildlife damage. RESULTS: We determined crop‐raiding hot spots across the USA over the 2015–2019 period and identified the eastern and southern regions of the USA as being the most susceptible to wildlife damage. We estimated lower bounds for dollar and percent losses attributable to wildlife to these four crops. The combined loss across four crops was estimated at $592.6 million. The highest total estimated losses to wildlife were incurred by soybeans ($323.9 million) and corn ($194.0 million) and the highest percentage losses were estimated for soybeans (0.87%) and cotton (0.72%). CONCLUSION: We believe the proposed method is a reliable way to evaluate geographic and temporal heterogeneity in damages for the coming years. Accurate information on damages benefits various management agencies by allowing them to allocate management resources to crops and regions where the problem is relatively severe. A better understanding of damage heterogeneity can also help guide research and development of new management techniques. [ABSTRACT FROM AUTHOR]

The participation in the Euro area and the current financial crisis substantially conditioned the development of the Portuguese banking industry, for which is expected a continuous fall in income and a growing competitive pressure, improving the need to look carefully to issues as efficiency as an essential survival factor. Efficiency indicators of the main banks operating in Portugal were measured through DEA methodology. The application of two-stage models allowed circumventing the usual problems inherent to the coexistence of the production and intermediation approaches. The application of regression for proportions, more appropriate than traditional linear and Tobit regressions, to deal with the fractional nature of the DEA scores, allowed the identification of efficiency determinant factors for the main banks operating in Portugal. The fractional regression models demonstrate evidence of improved specification comparing to traditional regression models. The variables that appear to major influence on overall efficiency are internationalization, size and type of ownership of capital.

Introduction: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. Methodology: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. Results: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Conclusions: Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the last two decades there was great attention for improved performance in the public sector. We know how important a good judicial system is: it permits to keep the peaceful coexistence between the citizens of a nation and, above all, the rights and duties that are necessary for each. In this paper our aim is to examine the Italian judicial efficiency, to check any territorial differences. For this purpose we use a two stage data envelopment analysis (DEA) model. In the first stage we evaluate performance of the 140 Ordinary Courts by mean of DEA, while in the second stage we explore determinant of efficiency/inefficiency using fractional regression model. Before doing this, we will prepare the reader presenting the basic concepts of efficacy and efficiency, explaining the method DEA and its various applications and finally we will also talk about the literature on the efficiency of the judiciary, which is a topic in Italy, despite the numerous ISTAT data, it did not find the right recognition. A bit will also be carried out on the international literature on the subject. At the end of all this will be shown in practical terms what has been done. [ABSTRACT FROM AUTHOR]

Companies try to demonstrate to government and stakeholders their compliance with tax regulations. However, in recent times, there has been increasing awareness in Nigeria that, tax aggressiveness strategies by board of directors of companies has a detrimental impact on government revenue, making it more difficult for government to provide the basic infrastructural facilities to the citizenry. This study, therefore, examines the relationship between board characteristics and corporate tax aggressiveness in Nigeria. The research design adopted for the study is the correlational research design. A sample of 60 non-financial services companies listed on the Nigerian Stock Exchange (NSE) for the years 2008 - 2015 were judgementally selected, and the data were analysed, using the Fractional regression technique. The regression results indicated that corporate board characteristics, such as board independence, board size, and audit firm size were significant factors influencing corporate tax aggressiveness in Nigeria, while the inclusion of a female on the board did not influence corporate tax aggressiveness. The paper recommends that appropriate corporate governance structures should be put in place to curb the menace of corporate tax aggressiveness in Nigeria, especially when it does not add value to the stakeholders. [ABSTRACT FROM AUTHOR]

Food security is a global challenge. With rising world population and demand for food being compounded by resource and arable land constraints, raising the efficiency of food production and use has become increasingly important. While much of the research on food security is focused on farm efficiency and productivity, most neglect post-harvest (PH) handling which is critical in determining the availability of food. In this study, we employ the network Data Envelopment Analysis (DEA) model to evaluate the PH efficiency of milling, using data from Kenya’s rice processing industry. The results show lower efficiency scores when using a network DEA model, which reflects its greater discriminatory power when compared to the standard DEA approach. The study also quantified sources of productive efficiency using a fractional regression model and identified storage space and distance to market as having an impact on drying efficiency; while experience, age of mill, servicing and energy type influenced milling efficiency. The results suggest that policy makers should focus on investing in drying technologies and storage facilities to improve drying efficiency. To improve milling efficiency, policy recommendations include enhancing millers’ access to better technologies, investing in reliable sources of energy and providing PH handling workshops to reduce PH losses. [ABSTRACT FROM AUTHOR]

Economists often treat pesticide resistance as a common-pool resource problem. While pecuniary economic incentives are the standard prescription for open-access market failures arising from such resources, non-pecuniary behavioral approaches (e.g., "nudges") are also effective in some cases. Yet non-pecuniary instruments have not previously been evaluated for managing pesticide resistance. I empirically evaluate the performance of such an intervention to manage pest resistance to genetically engineered Bacillus thuringiensis (Bt) corn. The U.S. Environmental Protection Agency permits sale of Bt seed conditional on seed producers compelling customers to plant mandated levels of non-Bt refuge to delay the evolution of Bt resistance. Because of compliance challenges, the Bt seed producer Monsanto piloted a social marketing program to promote refuge in 17 North Carolina counties in 2013-2014. Using 2013-2016 sales data, I use difference-in-differences, fractional regression, discrete changes-in-changes, and matched differences econometric models to identify the average treatment effect of the program on refuge planting. Results suggest that if it had covered all corn growers in North Carolina, the intervention would have led the average grower to plant between 2.6% (preferred estimate) and 5.8% more refuge in 2014 compared to the counterfactual. The program increased by at least 12% the average probability of planting any refuge in 2014. I find little evidence that effects of the program persisted in subsequent years after cessation, nor that the program increased compliance with mandated refuge thresholds. Informed by behavioral economics research on other environmental and resource policies, I discuss the implications of these findings for pesticide resistance management. [ABSTRACT FROM AUTHOR]

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We study the effect of ex ante expected wages rather than realized wages on the decision to pursue a nursing college education in Switzerland. On average, wage expectations reflect observed market wages, but with large dispersion among individuals. We collected data from a full cohort of healthcare trainees in their third year of training on upper-secondary level. Those trainees who expected a greater return from nursing college (tertiary level) were more likely to enroll in nursing college later on; the effect is substantial and almost orthogonal to individuals’ characteristics. This indicates that policies that increase returns from studying nursing can indeed attract new students to reduce the shortage of nurses. Subjective ex ante wage expectation data are useful in predicting the decision to enroll in college. [ABSTRACT FROM AUTHOR]

Universities in Vietnam are inclined to have an inefficient mix of input resources because of rigidities restricting the adoption of advanced teaching technology. There has been a deferral of the adoption of new teaching technologies to meet the rest of the world's higher education standards. Using an input-orientated data envelopment analysis (DEA) method, technical, scale and mix efficiency indicators are estimated for 112 Vietnamese universities over the period 2011-2013 using the Färe-Primont index. The results indicate that the technical, scale and mix efficiency indicators are 0.784, 0.866 and 0.829, respectively. Using the fractional regression model, it is found that location, age, ownership and financial capacity have significant influences on the mix efficiency indicators of universities. [ABSTRACT FROM AUTHOR]

In this article, we suggest simple moment-based estimators to deal with unobserved heterogeneity in a special class of nonlinear regression models that includes as main particular cases exponential models for nonnegative responses and logit and complementary loglog models for fractional responses. The proposed estimators: (i) treat observed and omitted covariates in a similar manner; (ii) can deal with boundary outcomes; (iii) accommodate endogenous explanatory variables without requiring knowledge on the reduced form model, although such information may be easily incorporated in the estimation process; (iv) do not require distributional assumptions on the unobservables, a conditional mean assumption being enough for consistent estimation of the structural parameters; and (v) under the additional assumption that the dependence between observables and unobservables is restricted to the conditional mean, produce consistent estimators of partial effects conditional only on observables. [ABSTRACT FROM AUTHOR]

This paper assesses the efficiency of rain-fed cereals crops grown in Southern Spain. The proposed approach uses a two-stage Data Envelopment Analysis (DEA) plus regression approach. In the first stage a Slacks-based Inefficiency (SBI) DEA model is used to project conventional and organic cropping systems onto the efficient frontier. The results of the efficiency analysis show that conventional production is more inefficient than organic production and that the main sources of inefficiency in the case of conventional production correspond to excessive input consumption and GHG emissions. In the case of organic production, the inefficiency comes from output shortfalls. It is shown that reducing inefficiency would reduce the amount of GHG emitted per unit of fresh matter yielded. This potential gain is more pronounced in the case of conventional production but also occurs for organic production. In the second stage, the crops efficiency scores are regressed against some exogenous variables using a fractional regression model. The regression results confirm that organic production significantly decreases inefficiency and they also indicate that the larger the farm, the larger the inefficiency and that growing barley is more inefficient than wheat. [ABSTRACT FROM AUTHOR]

The moderating role of corporate life cycle stages with the impact of relative economic value added (EVA) indicator on corporate social responsibility participation index (CSRPI). Chinese A-share listed companies are investigated. The CSRPI weights are calculated by Analytic Network Process. Fractional regression with interaction is used. The corporate life cycle stages moderate the relationship between relative EVA measure and CSRPI. Surprisingly, this impact is confirmed for companies at non-mature stages, but not for mature companies. Model type, weights and corporate life cycle robustness were confirmed. The findings have implications for stakeholders in understanding companies' social behaviour in the Chinese market. • The moderating role of corporate life cycle on corporate social responsibility in China • Corporate social responsibility participation index construction by analytic network process • Fractional regression with moderating effect application, model robustness verification • Confirmation of moderating effect of the corporate life cycle only for non-mature companies [ABSTRACT FROM AUTHOR]

This article uses 2017 Brazilian agricultural census data (aggregated at county level) to evaluate the impact of external factors on agricultural efficiency in Brazil. The external factors are defined as access to credit, participation in cooperatives, proportion of literacy, technical assistance, and environmental production practices. All variables are transformed as the log of the municipal (county) rank. The response variable is defined as the free disposal hull (FDH) conditional ratio and a one-inflated beta regression is estimated. The results show that in counties where the FDH ratio is equal to 1, cooperatives have the biggest impact on efficiency, and where the FDH ratio is not equal to 1, environmental practices, which are most often associated with technology, have the largest effect on efficiency. Finally, the regions that are expected to see the largest gains in efficiency with increases in external factors are the Northeastern and the Northern regions of Brazil. • We assessed the effect of covariates on the production process of the Brazilian agriculture in 2017. • The response variable is an FDH conditional ratio measure of efficiency computed for county data. • We modeled the ratio as a beta-inflated distribution, using maximum likelihood. • The highest expected gains from public policies will occur in the North and Northeast. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two-part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small. [ABSTRACT FROM AUTHOR]

The aim of this research is to investigate the operational efficiency and potential output increase scenarios of 21 small and medium sized airports (SMA) located in 10 different European countries. A key feature of the proposed Data Envelopment Analysis (DEA) models is the consideration of two novel inputs related to the number of airlines and scheduled routes operating at each airport, in addition to the more common capital non-discretionary inputs (runways, boarding gates and apron). These novel inputs are a way of considering the effects of the air transport market on airports' performance and growth prospects. In particular, a model to estimate the growth in passenger numbers, aircraft movements and cargo that can be achieved by an airport through increasing its number of airlines and routes, and assuming an efficient operation, is presented. Also, a multi-objective DEA model is proposed to explore the possible trade-offs in the output Pareto efficient frontier of a given airport. In addition, a second stage fractional regression model specific for explaining DEA efficiency scores is conducted and the results suggest that ownership structure and hub airport status are significant factors for explaining variations in the operational efficiency of European SMA. [ABSTRACT FROM AUTHOR]

In this article, we use Brazilian census data (1995/1996 and 2006) to model agricultural production at the state level in Brazil. Cost-efficiency measurements are computed using data envelopment analysis techniques, and the response is assessed via fractional regression. We examine the effects of time, geographic region, education, and investment in agricultural research on economic efficiency. We found that investment in agricultural research and regional dummies have a significant effect on efficiency measurements. On average, South and Southeast states are more efficient than other states. An increase in cost efficiency can be accomplished through investment in agricultural research. [ABSTRACT FROM AUTHOR]

Summary The study investigates whether African agricultural efficiency levels have been improving or not and what drives them over the years based on 442 frontier studies using meta-regression analysis. The results show that the mean efficiency estimates from studies decrease significantly as year of survey in the primary study increases. Also studies published in Journals, with parametric specification and with panel data produced significantly higher efficiency estimates, while those with a focus on grain crops reported significantly lower efficiency estimates. Other results show that education, followed by experience, extension, and credit are the major drivers of agricultural efficiency levels in Africa. [ABSTRACT FROM AUTHOR]

This article measures and explains the technical efficiency (TE) of 371 dairy farms located in 17 districts in East African countries. Three output and 10 input types were used to calculate the efficiency score for each farm. A two-stage analysis was conducted to measure and explain the efficiency scores. Firstly, the efficiency scores were measured by a data envelopment analysis (DEA) approach, which was implemented using a linear programming (LP) method. About 18 per cent of the farms were fully productive, each with an efficiency score of unity, which means this group is currently operating on the production possibility frontier. About 32 per cent of the farms had efficiency scores below 0.25, which means that about a third of the dairy farms would need to expand their dairy production by at least 75 per cent from the current level without any increase in the level of inputs. Secondly, a fractional regression method was used to explain the efficiency scores by relating them to a range of explanatory variables. The findings indicate that technology adoption factors, such as the existence of improved breeds, and feed and fodder innovations (e.g., growing legumes), have positive and statistically significant effects on the level of efficiency. Similarly, zero-grazing seems to have positive and highly significant effects. As far as marketing variables are concerned, selling milk to individual consumers or organisations seems to contribute positively and more significantly to dairy efficiency than other marketing outlets such as traders or chilling plants. Membership of a dairy cooperative has a positive effect but is not statistically significant. [ABSTRACT FROM PUBLISHER]

• Irrigation water overused on average by 37.3 % on 142 rice fields evaluated. • Sixty of the 142 rice fields over-applied irrigation water in excess of 50 %. • Diesel and labor were overused on average by 54.6 % and 58.9 %, respectively. • Irrigation efficiency was highly correlated with diesel and labor efficiency. • Management improving irrigation efficiency also improved diesel and labor efficiency. Groundwater from the Mississippi River Valley alluvial aquifer is an essential resource for agricultural irrigation in Arkansas, but groundwater from this valuable resource is being withdrawn more rapidly than aquifer recharge in many parts of the state. Rice accounts for a significant portion of groundwater withdrawn from the aquifer. Rice is also a high-cost crop relative to other field crops. This study uses data envelopment analysis and non-radial technical efficiency to evaluate irrigation water efficiency along with the efficiency of other key rice production inputs using data from 142 rice fields enrolled in the University of Arkansas Rice Research Verification Program (RRVP). The study also evaluates the impacts of management practices on input use efficiency using fractional regression. This study differs from most other studies that focus specifically on irrigation water efficiency in that efficiencies of other related rice inputs are also evaluated. We found irrigation water was overused on average by 37.3 % across the 142 fields, with 60 fields (42.3 % of all fields evaluated) over-applying irrigation water by over 50 %. Other rice inputs identified as highly inefficient included herbicides, diesel, and labor, which were overused on average by 46.4 %, 54.6 %, and 58.9 %, respectively, across the 142 fields. Results of the fractional regression analysis revealed management practices significantly improving irrigation water efficiency also significantly improved diesel and labor efficiency as well as overall field efficiency. [ABSTRACT FROM AUTHOR]

This paper analyses the operational efficiency of fish farming across EU Member States using a two-stage Data Envelopment Analysis (DEA) approach. In the first stage, a non-oriented Slacks-Based Measure of efficiency (SBM) DEA model is used to compute efficiency scores in marine and freshwater finfish and shellfish aquaculture subsectors for different EU countries during the period 2014–2016. In a second stage, these scores are processed by standard, censored and fractional regression models to test the effects of some exogenous variables. Between 57% and 74% of the observations, depending on the subsector considered, were efficient. The average technical efficiency was 0.918 for freshwater finfish, 0.885 for marine finfish and 0.802 for shellfish. Extending the best practices to the inefficient countries would involve a reduction of feed costs (2.9% - 4.3%), livestock costs (9.0% - 11.8%), energy costs (2.4% - 25.3%), repairing costs (3.7% - 13.8%) and other operating costs (4.3% - 13.8%)at the same time that an improvement in production value totalling 0.03% for freshwater finfish, 2.13% for marine finfish and 0.37% for shellfish. As regards productivity change in the period under study, there has been a productivity regress in the case of freshwater finfish, productivity increase in the case of marine finfish and an initial productivity increase between 2014 and 2015 followed by a slight decrease between 2015 and 2016 in the case of shellfish. Results also indicate that countries specializing in cultivating freshwater and marine populations are more likely to be on the efficient frontier and that technical efficiency seems to be influenced by the size of the country's gross domestic product and capture fisheries. • Data Envelopment Analysis computes efficiency scores in marine and freshwater finfish and shellfish EU aquaculture subsectors • Potential feed costs, livestock costs, repairing costs and other operation costs reductions are detected and quantified • Best practices in production value are identified in finfish and shellfish sectors • Countries specializing in cultivating freshwater and marine populations are likely to be on the efficient technical frontier • EU aquaculture production efficiency level is not completely a consequence of managerial inefficiency [ABSTRACT FROM AUTHOR]