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.

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 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.

This study developed a cell-based spatial optimization model compatible with the ArcGIS platform, termed Dynamically Dimensioned Search Landscape Optimization Planning model (DDSLOP), for landscape planning. The development of the proposed model was based on the Dynamically Dimensioned Search Algorithm, which can efficiently find an optimal global solution within the massive solution space inherent to multi-dimensional analysis. Therefore, the DDSLOP model can reveal landscape pattern scenarios suited to specific managerial purposes at a cellular level. To evaluate the DDSLOP model, we applied it to a landscape planning initiative that focused on the conservation of three bird species in the National Taiwan University Highland Experimental Farm (NTU-HEF). We compared the proposed model with the Land-Use Pattern Optimization-library (LUPOlib), which was used in the optimization of landscapes at a patch level. The results of the comparison revealed that our fine scale optimization method has better flexibility, and can therefore form landscape structures, which, overall, provides not only better individual habitats for the target species, but also landscape patterns that foster high habitat connectivity, both important aspects of conservation efforts.

Remediation prioritization frequently falls short of systematically evaluating the underlying ecological value of different sites. This study presents a novel approach to delineating sites that are both contaminated by any of eight heavy metals and have high habitat value to high-priority species. The conservation priority of each planning site herein was based on the projected distributions of eight protected bird species, simulated using 900 outputs of species distribution models (SDMs) and the subsequent application of a systematic conservation tool. The distributions of heavy metal concentrations were generated using a geostatistical joint-simulation approach. The uncertainties in the heavy metal distributions were quantified in terms of variability among 1000 realization sets. Finally, a novel remediation decision-making approach was presented for delineating contaminated sites in need of remediation based on the spatial uncertainties of multiple realizations and the priorities of conservation areas. The results thus obtained demonstrate that up to 42% of areas of high conservation priority are also contaminated by one or more of the heavy metal contaminants of interest. Moreover, as the proportion of the land for proposed remediated increased, the projected area of the pollution-free habitat also increased. Overall uncertainty, in terms of the false positive contamination rate, also increased. These results indicate that the proposed decision-making approach successfully accounted for the intrinsic trade-offs among a high number of pollution-free habitats, low false positive rates and robustness of expected decision outcomes.