China’s agricultural prospects and challenges: Report on scenario simulations until 2030 with the Chinagro welfare model covering national, regional and county level

The report describes prospects and challenges for Chinese agriculture until 2030 under different scenarios, using the Chinagro welfare model. A scenario is defined as a coherent set of assumptions about exogenous driving forces (farm land, population, non-agricultural growth, world prices etc.), derived from the literature and own assessments. Under these assumptions, simulations with the Chinagro model analyze the price-based interaction between the supply behavior of farmers, the demand behavior of consumers and the determination of trade flows by merchants. The outcomes from the Baseline scenario seem reassuring in that foreign imports remain moderate relative to China’s size, though quite large as fraction of world trade. It would be possible to feed people as well as animals without excessive imports. There is even a potential for significant export flows of vegetables and fruits. Regarding concerns, the trends in per capita agricultural value added are problematic, because they stay in all regions behind per capita value added outside agriculture, albeit that they are rising steadily. This leads to growing disparity in per capita incomes within and across regions. The mounting environmental pressure from fertilizer losses and unused manure surpluses is another cause of concern. The second scenario, the Trade liberalization scenario, appears to hurt farm incomes more than it benefits them and to raise the gap with non-agriculture, also because food becomes cheaper in urban areas. Hence, it highlights the difficult choice between economic efficiency and poverty alleviation that agricultural policy makers often face. The High income growth scenario reinforces the national food self-sufficiency result of the baseline simulation. Even with meat demand higher than under the baseline, levels of imports remain manageable. The High R&D scenario shows that a considerable reduction in dependence on agricultural imports is possible. However, a substantial part of the gains will accrue to consumers rather than to farmers, due to price reductions. Finally, the Enhanced irrigation scenario shows outcomes similar to those of the high R&D scenario. Here also the agricultural trade balance improves and consumer welfare improves, but farmers have to cope with drops in prices, and those who do not benefit from land improvement, only experience losses through falling prices. The present report is written at the onset of the CATSEI-project that will analyze policy packages with more specificity and detail after implementing the following model improvements. First, the impact of China’s imports and exports on world markets will be represented explicitly. Second, the developments outside agriculture in rural areas will be accounted for endogenously, particularly to represent farm revenue from off-farm employment. Third, the trade and transportation margins between farm-gates and markets will be made dependent on the relative flexibility of the actors (farmers, processors, traders) along the chain. Finally, the various techniques to identify more efficient and more sustainable use of scarce water and nutrients and to address health risks will appear more explicitly

W3LS: Evaluation framework for World Wide Web learning

An evaluation framework for World Wide Web learning environments has been developed. The W3LS(WWW Learning Support) evaluation framework presented in this article is meant to support the evaluationof the actual use of Web learning environments. It indicates how the evaluation can be set up usingquestionnaires and interviews among other methods. The major evaluation aspects and relevant 'stakeholders' are identified. First results of cases using the W3LS evaluation framework are reported from different Higher Education institutes in the Netherlands. The usability of the framework is evaluated, and future developments in the evaluation of Web learning in Higher Education in the Netherlands are discussed

The Impact of Climate Change on Water Availability and Recharge of Aquifers in the Jordan River Basin

Climate change can seriously affect the Middle East region by reduced and erratic rainfall. Formulating appropriate coping policies should account for local effects and changing flows interconnecting spatial units. We apply statistical downscaling techniques of coarse global circulation models to predict future rainfall patterns in the Yarmouk Basin, using a linear regression to extrapolate these results to the entire Jordan River Basin (JRB). Using a detailed water economy model for the JRB we predict rainfall patterns to evaluate the impact of climate change on agriculture and groundwater recharge. For the JRB, rainfall in 2050 will be around 10% lower than present precipitation, but with substantial spatial spreading. An overall reduction of net revenue from crop cultivation is estimated at 150 million USD, with major losses in Israel, Jordan, and the West Bank; Syrian revenues will slightly increase. The recharge of groundwater is affected negatively, and outflow to the Dead Sea is substantially lower, leading to further increases in salinization

Interventions for erythema nodosum leprosum (Protocol)

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:To assess the effects of any therapy or treatment used in the management of ENL.<br/

Interventions for erythema nodosum leprosum (Protocol)

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:To assess the effects of any therapy or treatment used in the management of ENL.<br/

Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network

Images are an important data source for diagnosis and treatment of oral diseases. The manual classification of images may lead to misdiagnosis or mistreatment due to subjective errors. In this paper an image classification model based on Convolutional Neural Network is applied to Quantitative Light-induced Fluorescence images. The deep neural network outperforms other state of the art shallow classification models in predicting labels derived from three different dental plaque assessment scores. The model directly benefits from multi-channel representation of the images resulting in improved performance when, besides the Red colour channel, additional Green and Blue colour channels are used.Comment: Full version of ICANN 2017 submissio