Blockchain technology, while still challenged with key limitations, is a transformative Information and Communications Technology (ICT) that has changed our notion of trust. Improved efficiencies for agricultural sustainable development has been demonstrated when ICT-enabled farms have access to knowledge banks and other digital resources. UN FAO-recommended ICT e-agricultural infrastructure components are a confluence of ICT and blockchain technology requirements. When ICT e-agricultural systems with blockchain infrastructure are immutable and distributed ledger systems for record management, baseline agricultural environmental data integrity is safeguarded for those who participate in transparent data management. This paper reviewed blockchain-based concepts associated with ICT-based technology. Moreover, a model ICT e-agriculture system with a blockchain infrastructure is proposed for use at the local and regional scale. To determine context specific technical and social requirements of blockchain technology for ICT e-agriculture systems, an evaluation tool is presented. The proposed system and tool can be evaluated and applied to further developments of e-agriculture systems.

Model evaluation metrics play a critical role in the selection of adequate species distribution models for conservation and for any application of species distribution modelling (SDM) in general. The responses of these metrics to modelling conditions, however, are rarely taken into account. This leads to inadequate model selection, downstream analyses and uniformed decisions. To aid modellers in critically assessing modelling conditions when choosing and interpreting model evaluation metrics, we analysed the responses of the True Skill Statistic (TSS) under a variety of presence-background modelling conditions using purely theoretical scenarios. We then compared these responses with those of two evaluation metrics commonly applied in the field of meteorology which have potential for use in SDM: the Odds Ratio Skill Score (ORSS) and the Symmetric Extremal Dependence Index (SEDI). We demonstrate that (1) large cell number totals in the confusion matrix, which is strongly biased towards ‘true’ absences in presence-background SDM and (2) low prevalence both compromise model evaluation with TSS. This is since (1) TSS fails to differentiate useful from random models at extreme prevalence levels if the confusion matrix cell number total exceeds ~30,000 cells and (2) TSS converges to hit rate (sensitivity) when prevalence is lower than ~2.5%. We conclude that SEDI is optimal for most presence-background SDM initiatives. Further, ORSS may provide a better alternative if absence data are available or if equal error weighting is strictly required.

Abstract:In order to guarantee high-quality agricultural products and food safety, efforts must be made to manage and maintain healthy agricultural environments under the myriad of risks that they face. Three central system components of sustainable agricultural management schemes are real-time monitoring, decision-making, and remote access. Information and Communications Technology (ICT) systems are a convenient means of providing both these and other functions, such as wireless sensor networking, mobile phone applications, etc., to agricultural management schemes. ICT systems have significantly improved in recent years and have been widely used in many fields, including environmental monitoring and management. Moreover, ICT could benefit agricultural environment management by providing a platform for collaboration between researchers and stakeholders, thereby improving agricultural practices and environments. This article reviews and discusses the way in which ICT can efficiently improve monitoring systems and risk assessments of agricultural environment monitoring, as well as the technological and methodological improvements of ICT systems. Finally, we develop and apply an ICT system, referred to as the agricultural environment protection system-comprised of a cloud, six E-platforms, three mobile devices, automatic monitoring devices, indigenous wireless sensor nodes, and gateways in agricultural networks-to a case study in the Taoyuan irrigation district, which acts as a pilot area in Taiwan. Through the system, we use all available information from the interdisciplinary structured cloud database to classify the focal area into different agricultural environmental risk zones. We also conducted further analysis based on a hierarchical approach in order to classify the agricultural environments in the study area, to allocate additional sampling with resin packages and mobile devices, as well as to assist decision makers and stakeholders. The main contributions that the system provides include a technical innovation platform (suitable for integrating innovations), economic benefits, and societal benefits.

Systematic conservation planning (SCP) deals with a delicate interplay of competing interests and has far-reaching impacts for all stakeholders and systems involved. While SCP has traditionally attempted to conserve ecosystem services that benefit ecological systems, public perceptions of conservation initiatives influence their ultimate feasibility and sustainability. In an attempt to balance ecological integrity, social utility, and urban development, this study develops a framework that applies four popular models to represent these competing factors, including two ecosystem services models-InVEST (Integrated Valuation of Environmental Services and Tradeoffs) for biophysical services (BpS), and SolVES (Social Values for Ecosystem Services) for social values (SV); a land use and land cover (LULC) suitability model; and Zonation for delimiting high priority areas. We also analyze a number of conservation scenarios that consider varying levels of urban development. While BpS are distributed with considerable spatial variability, SV spatially overlap. Approximately 6% of the area was identified as having both high BpS and SV, whereas a further 24.5% of the area was identified as either high BpS low SV or vise-versa. Urban development scenarios affected the conservation area selection drastically. These results indicate tradeoffs and potential synergies between development, SV, and BpS. Our findings suggest that the information provided by the proposed framework can assist in finding solutions to social-ecological planning complexities that serve multiple stakeholders.

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Ammonia oxidation is crucial in nitrogen removal and global nitrogen dynamics since it is the first step of the nitrification process. In this review, we focus on the distribution and community structure of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in East Asian paddy soils with variable soil properties. The available East Asian paddy soil data shows that the ammonium concentration and pH ranges from 0.4 to 370 mg/kg and 5.1 to 8.2, respectively. Our meta-analysis suggest that AOA specific gene sequences are generally more abundant than those of AOB in both acidic and alkaline paddy soils, where Nitrosophaera and Nitrosospira amoA clusters mainly dominate the microbial community, respectively. In addition, the contribution of ammonia oxidizers to the nitrification process has been demonstrated using DNA-SIP (DNA-based stable-isotope probing); the results of these studies indicate that pH is the most important factor in niche separation of AOA and AOB under a variety of edaphic conditions. Finally, we discuss a number of other environmental variables that affect the abundance, distribution, and activity of AOA and AOB in East Asian paddy soils.

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Tree species in mountainous areas are expected to shift their distribution upward in elevation in response to climate change, calling for a potential redesign of existing protected areas. This study aims to predict whether or not the distributions of two high-mountain tree species, Abies (Abies kawakamii) and Tsuga (Tsuga chinensis var. formosana), will significantly shift upward due to temperature change, and whether current protected areas will be suitable for conserving these species. Future temperature change was projected for 15 different future scenarios produced from five global climate models. Shifts in Abies and Tsuga distributions were then predicted through the use of species distribution models (SDMs) which included occurrence data of Abies and Tsuga, as well as seasonal temperature, and elevation. The 25 km × 25 km downscaled General Circulation Model (GCMs) data for 2020-2039 produced by the Taiwan Climate Change Projection and Information Platform OPEN ACCESSForests 2014, 5 2883 was adopted in this study. Habitat suitability in the study area was calculated using maximum entropy model under different climatic scenarios. A bootstrap method was applied to assess the parameter uncertainty of the maximum entropy model. In comparison to the baseline projection, we found that there are significant differences in suitable habitat distributions for Abies and Tsuga under seven of the 15 scenarios. The results suggest that mountainous ecosystems will be substantially impacted by climate change. We also found that the uncertainty originating from GCMs and the parameters of the SDM contribute most to the overall level of variability in species distributions. Finally, based on the uncertainty analysis and the shift in habitat suitability, we applied systematic conservation planning approaches to identify suitable areas to add to Taiwan's protected area network.