[学术文献] Neoliberalization and alternative food movements: Vermont’s ‘right to know’ GMO campaign 进入全文
Journal of Rural Studies
The regulatory restructuring of global agrifood production and distribution ushered in during the 1980s era of neoliberalization (e.g. trade liberalization, strengthened intellectual property rights, the promotion of the private sector, reduced state social welfare protections) has prompted a number of changes in food provisioning with significant social repercussions, from rising obesity levels and e-coli outbreaks to impoverished Kenyan's producing beans for rich westerners. In consequence, an almost equally defining characteristic of this food system has been the rising amount of resistance from a plethora of alternative food movements—e.g. anti-biotechnology, organic, fair trade, buy local, and slow food, amongst many—to what they consider the system's social irrationalities. The fact that this system has created as many detractors as it has social concerns has not gone unnoticed by agrifood scholars. Not surprisingly, many of these scholars have cast these alternative food movements [AFMs] in the context of Karl Polanyi's (1944) concept of the double movement, whereby market irrationalities will induce a societal protective measure, to prevent “annihilat[ion] by the action of the self-regulating market” (1944: 249; see, for example, Carroll, 2016; Friedmann and McNair, 2008; Raynolds, 2012).
Journal of Food Science and Technology
The -oryzanol contents and the composition of steryl ferulates distributed in Japanese pigmented rice varieties were investigated using the high-performance liquid chromatography-ultraviolet detection method for the purpose of expanding their utilisation. The average -oryzanol content in nine black-purple, four red, four green and three brown rice varieties was 54.2, 47.3, 44.3 and 43.3mg -oryzanol equivalent/100g dried weight, respectively. Among the nine varieties of black-purple rice, five varieties showed steryl ferulate composition similar to that of brown, red and green varieties. In contrast, the composition of steryl ferulates in other four black-purple rice varieties was partially specific and was characterised by a low amount of campesteryl ferulate and high of campestanyl ferulate and stigmastanyl ferulate. The latter two steryl ferulates have been recognised as minor components of -oryzanol in rice and as major components in wheat and corn. These results indicate that the compositions of steryl ferulates vary among Japanese black-purple rice varieties.
Jasmonates (JAs) are crucial to the coordination of plant stress responses and development. JA signaling depends on JASMONATE-ZIM DOMAIN (JAZ) proteins that are destroyed by the SCFCOI1-mediated 26S proteasome when the JAZ co-receptor COI1 binds active JA or the JA-mimicking phytotoxin coronatine (COR). JAZ degradation releases JAZ-interacting transcription factors that can execute stress and growth responses. The JAZ proteins typically contain Jas motifs that undergo conformational changes during JA signal transduction and that are important for the JAZ-COI1 interaction and JAZ protein degradation. However, how alterations in the Jas motif and, in particular, the JAZ degron part of the motif, influence protein stability and plant development have not been well explored. To clarify this issue, we performed bioassays and genetic experiments to uncover the function of the OsJAZ1 degron in rice JA signaling. We found that substitution or deletion of core segments of the degron altered the OsJAZ1-OsCOI1b interaction in a COR-dependent manner. We show that these altered interactions function as a regulator for JA signaling during flower and root development. Our study therefore expands our understanding of how the JAZ degron functions, and provides the means to change the sensitivity and specificity of JA signaling in rice.
[学术文献] Exploring the characteristics and utilisation of Farm Management Information Systems (FMIS) in Germany 进入全文
Computers and Electronics in Agriculture
Digitisation in Agriculture is currently one of the most important ongoing developments to meet the growing economic, ecological and social demands in the agri-food sector in Germany. Consequently, the use of information and communication technologies (ICT) to collect, exchange and evaluate data from and between different stakeholders and systems has already established itself in the agricultural sector. However, the extent to which information systems are used and the kind of features they have at the farm enterprise level are not clear. To obtain further insight into this topic, a quantitative empirical approach was adopted. It was based on interview data from a web-based survey conducted throughout Germany at the beginning of 2018. In this survey, 329 questionnaires (representing an 8.4% response rate) were completed and evaluated using bivariate and multivariate methods. This paper aims to assign the surveyed farmers to two of the “five steps of digital evolution” model - from the “single product” to the “system of systems” - according to the stated characteristics and functions used in the Farm Management Information Systems (FMIS). According to that model, a single product (e.g. a tractor or a feed trough) develops at level 1 and becomes a “smart product” at level 2. The agricultural machine can now, for example, maintain precise tracking via integrated real-time kinematic (RTK) correction that enhances precise satellite navigation. At the third development stage, a “smart, connected product” is created, where the agricultural machine is networked with other systems. Level 4 represents an “intelligent product system”. The focus is no longer only on optimising a single process, but also on optimising process chains. At the last stage, “systems of systems” (i.e. “smart farming”), the networking of different data from diverse sources reaches the maximum level. 43 variables were used to conduct a two-step cluster analysis, in which two clusters could be identified within the sample. The farmers assigned to cluster 2 could be determined as “users of smart products” (58%) which represents level 2 of the model. These farmers are characterised by the fact that they use FMIS for the overall purpose of supporting the documentation, monitoring and planning of farm management processes. The highest level of digitisation in German agriculture was found to match level 3, what is known as “users of smart, connected products”. On this level, farmers that were assigned to the cluster 1 (42%) use their information systems to improve individual farm processes by connecting hardware, sensors, data storage and software in different ways.
Multimedia Tools and Applications
Rice is one of the world’s most popular food crops. Since its production is dependent on intensive water use, water management is critical to ensure sustainability of water resource. However, very limited data is available on water use in rice irrigation. In the present study, traditional machine learning methods have been used to predict the irrigation schedule of rice daily. The data of year 2013-2015 is used to train the models and to further optimise it. The data of 2016-2017 is used for testing the models. Correlation thresholds are used for feature selection which helps in reducing the number of input parameters from the initial 26 to final 11. The models estimated the crop water demand as a function of weather parameters. Results show that Adaboost performed consistently well with an average accuracy of 71% as compared to other models for predicting the irrigation schedule.
International Journal of Information Technology
Plant pathologists desire an accurate and reliable soybean plant disease diagnosis system. In this study, we propose an efficient soybean diseases identification method based on a transfer learning approach by using pretrained AlexNet and GoogleNet convolutional neural networks (CNNs). The proposed AlexNet and GoogleNet CNNs were trained using 649 and 550 image samples of diseased and healthy soybean leaves, respectively, to identify three soybean diseases. We used the five-fold cross-validation strategy. The proposed AlexNet and GoogleNet CNN-based models achieved an accuracy of 98.75% and 96.25%, respectively. This accuracy was considerably higher than that for conventional pattern recognition techniques. The experimental results for the identification of soybean diseases indicated that the proposed model achieved highest efficiency.