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.

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The sensitivity of SQRT model-estimated parameters varied over a temperature gradient whereas no variation in MMRT model-estimated parameters, in simulating temperature responses of soil nitrification over the temperature range, was observed.

Microbes can establish a pathogenetic or symbiotic relationship with plants in soil and aquatic ecosystems. Although change in bacterial and fungal community in soil and their interaction with plants have been widely studied, little is known about their community structure in hydroponic systems across plant growth stages under different nutrient treatments. This study used next-generation sequencing analysis to assess the temporal changes in melon rhizosphere bacterial and fungal community structure across six different nutrient treatments. We found significant changes in the microbial community composition (especially for bacteria) between growth stages (R = 0.25–0.63, p < 0.01) than nutrient treatments. Proteobacteria dominated the bacterial community at the phylum level across melon growth stages (59.8% ± 16.1%). The genera Chryseobacterium, Pseudomonas, and Massilia dominated the rhizosphere in the flowering and pollination stage, while Brevibacillius showed the highest relative abundance in the harvesting stage. However, the rhizosphere was dominated by uncultured fungal taxa, likely due to the application of fungicides (Ridomil MZ). Further, linear regression analysis revealed a weak influence of bacterial community structure on melon yield and quality, while fruit weight and quality moderately responded to Mg and K deficiency. Nevertheless, the relative abundance of bacterial genus Chryseobacterium in the vegetative stage showed a strong correlation with fruit weight (R2 = 0.75, p < 0.05), while genera Brevibacillus, Lysobacter, and Bosea in late growth stages strongly correlated with fruit sweetness. Overall, temporal variability in the microbial (especially bacterial) community structure exceeds the variability between nutrient treatments for the given range of nutrient gradient while having little influence on melon yield.

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

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.

Ammonia oxidizing archaea (AOA) and bacteria (AOB) are thought to contribute differently to soil nitrification, yet the extent to which their relative abundances influence the temperature response of nitrification is poorly understood. Here, we investigated the impact of different AOA to AOB ratios on soil nitrification potential (NP) across a temperature gradient from 4 °C to 40 °C in twenty different organic and inorganic fertilized soils. The temperature responses of different relative abundance of ammonia oxidizers for nitrification were modeled using square rate theory (SQRT) and macromolecular rate theory (MMRT) models. We found that the proportional nitrification rates at different temperatures varied among AOA to AOB ratios. Predicted by both models, an optimum temperature (Topt) for nitrification in AOA dominated soils was significantly higher than for soils where AOA and AOB abundances are within the same order of magnitude. Moreover, the change in heat capacity (ΔCP‡) associated with the temperature dependence of nitrification was positively correlated with Topt and significantly varied among the AOA to AOB ratios. The temperature ranges for NP decreased with increasing AOA abundance for both organic and inorganic fertilized soils. These results challenge the widely accepted approach of comparing NP rates in different soils at a fixed temperature. We conclude that a shift in AOA to AOB ratio in soils exhibits distinguished temperature-dependent characteristics that have an important impact on nitrification responses across the temperature gradient. The proposed approach benefits the accurate discernment of the true contribution of fertilized soils to nitrification for improvement of nitrogen management.