Self-awareness of driving impairment in patients with cataract or glaucoma

This study compared the driving performance of individuals with the eye diseases cataracts or glaucoma with age-matched controls, as well as the individual’s own perceptions of driving. Participants included drivers over the age of 50 years who had been diagnosed with glaucoma (n=29) or cataracts (n=33) and a control group with no ocular pathology (n=13). Driving performance was measured on a closed road circuit using a range of standardised measures of vehicle control and hazard recognition and avoidance, while visual performance was measured with a battery of tests including visual acuity, contrast sensitivity and visual fields. Perceptions of vision and driving were assessed using the Activities of Daily Vision Scale, Driver Behaviour Questionnaire and a driving exposure questionnaire. Driving performance was significantly poorer (p<0.05) for each of the ocular disease groups compared to the control group. Impaired contrast sensitivity and the higher disease severity scores (for the glaucoma group only) correlated most strongly with poorer driving performance. While participants with cataracts rated their vision significantly more poorly than those in the glaucoma and control groups, there were no significant differences between the participant groups rating of their own driving performance. These findings suggest that there is no direct relationship between self-rated driving ability and actual vision and driving performance. This has serious road safety implications

Evaluation of a new cropping option using a participatory approach with on-farm monitoring and simulation: a case study of spring-sown mungbeans

In the northern Australian cropping region, mungbean is commonly sown as an opportunity crop, usually on low soil water after a winter cereal, and consequently has a reputation for being a low yielding, high risk crop. Yield prospects could be improved and risks reduced if it was sown on soils with a higher soil water content, for instance in spring after a winter fallow. However, there is a lack of experience and confidence in alternative roles for mungbean in the farming system. This paper describes a research approach involving researchers, farmers, advisers, and grain traders in which on-farm monitoring of spring-sown commercial crops and cropping systems simulation with APSIM were used to explore yield prospects for a spring-sown crop after a winter fallow. The key elements of the approach are: (1) identification of possible options through simulation of scenarios, (2) testing the new practice with innovative farmers, and (3) monitoring of the management and performance of commercial crops and comparing yields with benchmarks estimated with a model. In this case, after 2 years of on-farm testing, spring-sown mungbean has been shown to have a potential for high returns in the northern cropping systems

How farming systems simulation can aid the development of more sustainable smallholder farming systems in southern Africa

Over the past 20 years, farming systems modelling has become an accessible tool for developing intervention strategies targeted at smallholder farmers in southern Africa. Applying the Agricultural Productions Systems sIMulator (APSIM) to credibly simulate key soil and crop processes in highly constrained, low yielding maize/legume systems has led to four distinct modes of use: (i) to add value to field experimentation and demonstration; (ii) in direct engagement with farmers; (iii) to explore key system constraints and opportunities with researchers and extension agencies; and (iv) in the generation of information for policy makers, bankers and insurance institutions. Examples of application in each of these modes are presented. Despite being demonstrated as an excellent tool for developing intervention strategies and extension material, the use of simulation is limited by a lack of competent local users. Better co-operation within the simulation community, sharing of climate, soil and crop parameterisation and validation datasets, and focussing of efforts on using models to benefit smallholder farmers are suggested as ways of increasing the use and relevance of simulation. Substantial investment in the training of agriculturalists and the further science development of systems simulation is required to tackle the enormous challenges facing agricultural development in the region

Simulation of growth and development of diverse legume species in APSIM

This paper describes the physiological basis and validation of a generic legume model as it applies to 4 species: chickpea (Cicer arietinum L.), mungbean (Vigna radiata (L.) Wilczek), peanut (Arachis hypogaeaL.), and lucerne (Medicago sativa L.). For each species, the key physiological parameters were derived from the literature and our own experimentation. The model was tested on an independent set of experiments, predominantly from the tropics and subtropics of Australia, varying in cultivar, sowing date, water regime (irrigated or dryland), row spacing, and plant population density. The model is an attempt to simulate crop growth and development with satisfactory comprehensiveness, without the necessity of defining a large number of parameters. A generic approach was adopted in recognition of the common underlying physiology and simulation approaches for many legume species. Simulation of grain yield explained 77, 81, and 70% of the variance (RMSD = 31, 98, and 46 g/m2) for mungbean (n = 40, observed mean = 123 g/m2), peanut (n = 30, 421 g/m2), and chickpea (n = 31, 196 g/m2), respectively. Biomass at maturity was simulated less accurately, explaining 64, 76, and 71% of the variance (RMSD = 134, 236, and 125 g/m2) for mungbean, peanut, and chickpea, respectively. RMSD for biomass in lucerne (n = 24) was 85 g/m2 with an R2 of 0.55. Simulation accuracy is similar to that achieved by single-crop models and suggests that the generic approach offers promise for simulating diverse legume species without loss of accuracy or physiological rigour

Food and agricultural innovation pathways for prosperity

This introduction to the special issue deploys a framework, inspired by realist synthesis and introduced in Section 1, that aims to untangle the contexts, mechanisms, and outcomes associated with investments that link poverty reduction and rural prosperity within a broad agri-food systems perspective. Section 2 considers changes in contexts: Where are agricultural research investments most likely to be an engine of poverty reduction? Over the past 25 years, there have been profound changes in the development context of most countries, necessitating an update on strategic insights for research investment priorities relevant for the economic, political, social, environmental, and structural realities of the early 21st Century. Section 2 briefly surveys changes in these structural aspects of poverty and development processes in low-income countries, with particular attention to new drivers (e.g., urbanization, climate change) that will be of increasing salience in the coming decades. In Section 3, we turn to mechanisms: What are the plausible impact pathways and what evidence exists to test their plausibility? Poor farmers in the developing world are often the stated focus of public sector agricultural research. However, farmers are not the only potential beneficiaries of agricultural research; rural landless laborers, stakeholders along food value chains, and the urban poor can also be major beneficiaries of such research. Thus, there are multiple, interacting pathways through which agricultural research can contribute to reductions in poverty and associated livelihood vulnerabilities. This paper introduces an ex ante set of 18 plausible impact pathways from agricultural research to rural prosperity outcomes, employing bibliometric methods to assess the evidence underpinning causal links. In Section 4, we revisit the concept of desired impacts: When we seek poverty reduction, what does that mean and what measures are needed to demonstrate impact? The papers in this special issue are intended to yield insights to inform improvements in agricultural research that seeks to reduce poverty. History indicates that equity of distribution of gains matters hugely, and thus the questions of “who wins?” and “who loses?” must be addressed. Moreover, our understanding(s) of “poverty” and the intended outcomes of development investments have become much richer over the past 25 years, incorporating more nuance regarding gender, community differences, and fundamental reconsideration of the meaning of poverty and prosperity that are not captured by simple head count income or even living standard measures

Role of modelling in improving nutrient efficiency in cropping systems

The applicability of models in addressing resource management issues in agriculture has been widely promoted by the research community, yet examples of real impacts of such modelling efforts on current farming practices are rare. Nevertheless, simulation models can compliment traditional field experimentation in researching alternative management options. The first objective of this paper is, therefore, to provide four case study examples of where models were used to help research issues relating to improved nutrient efficiency in low-input cropping systems. The first two cases addressed strategies of augmenting traditional farming practices with small applications of chemical fertilizer (N and P). The latter two cases explicitly addressed the question of what plant genetic traits can be beneficial in low-nutrient farming systems. In each of these case studies, the APSIM (Agricultural Production Systems Simulator) systems model was used to simulate the impacts of alternative crop management systems. The question of whether simulation models can assist the research community in contributing to purposeful change in farming practice is also addressed. Recent experiences in Australia are reported where simulation models have contributed to practice change by farmers. Finally, current initiatives aimed at testing whether models can also contribute to improving the nutrient efficiency of smallholder farmers in the SAT are discussed

Employee Stock Ownership and Financial Performance in European Countries: The Moderating Effects of Uncertainty Avoidance and Social Trust

This study investigates how the effect of employee stock ownership on financial performance may hinge on the diverse cultural and societal contexts of European countries. Based on agency and national culture theories, we hypothesize that the positive relationship between employee stock ownership and return on assets (ROA) is stronger in those nations with lower uncertainty avoidance and higher social trust. Using a multisource, time‐lagged, large‐scale dataset of 1,741 firms from 21 countries in Europe, our multilevel, random coefficient modeling analysis found evidence for these hypotheses, suggesting that uncertainty avoidance and social trust serve as important contextual cues in predicting the linkage between employee stock ownership and financial performance. Our supplemental analysis with distinction between the managerial and nonmanagerial employee stock ownership further indicates managerial employee stock ownership has a direct positive effect on ROA. Although nonmanagerial employee stock ownership had a nonsignificant association with ROA, the relationship was positive and significant when uncertainty avoidance was low and social trust was high. This research contributes to the existing literature by illuminating some of the contextual influences altering the effectiveness of employee stock ownership. Our findings also offer practical suggestions for effectively using employee stock ownership

Farmers, advisers and researchers learning together better management of crops and croplands

Summary. Farmers in the northeastern sub-tropics of Australia must cope with very high climatic variability in order to succeed in crop production. Their capacity for innovation was tapped by means of an on-farm research project that brought farmers, advisers and researchers together on the Darling Downs and in central Queensland. The researchers added value to the farmers&apos; own experiments on fertility and water use efficiency by soil and weather monitoring at specific sites and then using a simulation model of cropping systems to extend findings to a wider context of climate and soil. The advisers extended knowledge aquired from this experience via local farmer networks and have undertaken training in the use of simulation to support farmers&apos; management decisions. The experience described opens up possibilities for developing new, cost-effective ways for devising and testing improved farm management

Predicting growth and development of pigeonpea: flowering response to photoperiod

Data from sowing-date and other experiments conducted for nine cultivars at three locations ranging from 18300S to 278150N were analysed for photoperiod response. All cultivars were found to have a qualitative response to photoperiod. The results of the analysis show that cultivars previously reported to be "relatively insensitive" to photoperiod were, in fact, highly sensitive. Flowering in short-duration cultivars was delayed by up to a 100 days when daylength in the photoperiod-inductive phase exceeded a critical value. Medium- and long-duration cultivars delayed flowering by over 150 days in response to photoperiod. A model was able to predict this wide range in flowering dates

Predicting growth and development of pigeonpea: a simulation model

A simulation model of pigeonpea is described that is designed to simulate the development, growth, nitrogen accumulation and yield of a wide range of maturity types from extra-short to medium-duration in response to weather, soil conditions and agronomic management. Parameters of the model for phenological development, leaf area expansion, radiation interception, biomass accumulation and partitioning, crop water use, root growth and water extraction, and nitrogen accumulation are derived from published studies. In addition, the calibration exercise is described to derive the parameters accounting for the effect of plant population density on leaf area expansion. The model was tested on 38 data sets, not previously used to derive model parameters, collected at Patancheru, India. Data sets encompassed a wide range of plant type, sowing density, and seasons, grown on Alfisol and Vertisol soil types under dryland and irrigated conditions. The time to flowering and maturity from the extra-short to medium-duration types were simulated well, explaining 96 and 92% of the variance (RMSD=4.3 and 9.8 days), respectively. Total aboveground biomass was simulated with less accuracy, explaining 74% of the variance (RMSD=2056 kg ha-1) and grain yield was simulated explaining 76% of the variance (RMSD=332 kg ha-1). There remains scope for model improvement in the areas of waterlogging and testing on crop N accumulation. This pigeonpea module, when coupled with other crop, soil and management modules can be used to address a range of cropping systems issues