Agri-food trade has a profound impact on social stability and sustainable economic development. However, there are several technological problems in current agricultural product transactions. For example, it is almost impossible to improve the efficiency of transactions and maintain market stability. This paper designs a novel Food Trading System with COnsortium blockchaiN (FTSCON) to eliminate information asymmetry in the food trade, in order to establish a sustainable and credible trading environment, the system uses consortium blockchain technology to meet the challenge of different authentications and permissions for different roles in food trade. Meanwhile, we have used the online double auction mechanism to eliminate competition. We also have designed a improved Practical Byzantine Fault Tolerance (iPBFT) algorithm to improve efficiency. In addition, a case study based on a series of data from Shandong Province, China indicate that the FTSCON can achieve profit improvement of merchants. Therefore, the proposed system proved to have high commercial value.

The southeast Indian Ocean (SEIO) exhibits decadal variability in sea surface temperature (SST) with amplitudes of ~0.2–0.3 K and covaries with the central Pacific (r = −0.63 with Niño-4 index for 1975–2010). In this study, the generation mechanisms of decadal SST variability are explored using an ocean general circulation model (OGCM), and its impact on atmosphere is evaluated using an atmospheric general circulation model (AGCM). OGCM experiments reveal that Pacific forcing through the Indonesian Throughflow explains <20% of the total SST variability, and the contribution of local wind stress is also small. These wind-forced anomalies mainly occur near the Western Australian coast. The majority of SST variability is attributed to surface heat fluxes. The reduced upward turbulent heat flux (QT; latent plus sensible heat flux), owing to decreased wind speed and anomalous warm, moist air advection, is essential for the growth of warm SST anomalies (SSTAs). The warming causes reduction of low cloud cover that increases surface shortwave radiation (SWR) and further promotes the warming. However, the resultant high SST, along with the increased wind speed in the offshore area, enhances the upward QT and begins to cool the ocean. Warm SSTAs co-occur with cyclonic low-level wind anomalies in the SEIO and enhanced rainfall over Indonesia and northwest Australia. AGCM experiments suggest that although the tropical Pacific SST has strong effects on the SEIO region through atmospheric teleconnection, the cyclonic winds and increased rainfall are mainly caused by the SEIO warming through local air–sea interactions.

Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.

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Peach (Prunus persica) is one of the most important and widely grown fruit trees in China; however, perennial gummosis on trunks and branches is a major problem in peach orchards of Hubei Province, one of the most important peach production areas of China. In order to identify the gummosis-causing agents, diseased trunks and branches were collected from 11 peach orchards in Hubei Province. Fungal isolates were obtained from these samples, yielding three species: Botryosphaeria dothidea (anamorph Fusicoccum aesculi), B. rhodina (anamorph Lasiodiplodia theobromae), and B. obtusa (anamorph Diplodia seriata). They were identified based on conidial morphology and cultural characteristics, as well as analyses of nucleotide sequences of three genomic regions: the internal transcribed spacer region, a partial sequence of the β-tubulin gene, and the translation elongation factor 1-α gene. Fusicoccum aesculi was found in all 11 orchards but L. theobromae was found only in Shayang County in the Jingmen region and D. seriata only in Gong'an County in the Jingzhou region. Via artificial inoculation using mycelia on wounded twigs or branches, these three species were all found to be pathogenic, causing dark lesions on the twigs and branches and, sometimes, gum exudation from diseased parts. Isolates of L. theobromae were the most virulent and caused the largest lesions and most copious gummosis, and D. seriata had less gum than the other two species. This report represents the first description of L. theobromae and D. seriata as causal agents of gummosis on peach in China.

To limit global temperature rise, scientists have proposed significant potentials for climate change mitigation from protecting and managing natural systems. However, depending on the time taken for technology deployment and natural carbon gain, actual mitigation can be dramatically delayed, and total mitigation by 2030 or 2050 can be more than halved compared to the estimated potential. Delayed or lack of action on implementation would push back the timeline to reduce greenhouse gas emissions, largely undermining the Paris goals. Launching actions now and learning from past experience can help deliver climate mitigation and sustainable development goals.

Purpose Although the bioavailability of heavy metals has been widely investigated, little information is available on the spatial correlations of heavy metals in soil-rice systems at a regional scale. A study of heavy metals in soil-rice systems at a present rice production area could provide valuable information on the safety of rice production and provide guidelines beneficial to agriculture management and strategic sustainable agriculture in China and other rapidly developing regions in the world. The overall goals of this study were to identify the characteristics of metal fractions and their bioavailability to rice plants in the paddy fields of a present rice production region. Materials and methods In the rice harvest season (October 2006), 96 pairs of rice grain and rooted soil samples were collected from rice production area of Wenling in southeast Zhejiang province, China, which is one of the well-known electronic and electric waste (E-waste) recycling centers. Soil samples were analyzed for total heavy metal concentrations, metal fraction concentrations, and soil properties. Soil properties analyzed in this study included soil pH, electrical conductivity, organic matter, Fe oxides, and soil particle size distribution. Rice grain samples were analyzed for heavy metal concentrations. Multivariate statistical and geostatistical methods were applied to study the spatial dependence characteristics of metal fractions and their spatial correlation with uptake by rice plants in the rice production area and to identify the bioavailability of metal fractions to rice plants. Results and discussionThe paddy soils of the studied area showed Cd contamination and some paddy soils presented a potential Cu, Ni, and Zn risk. The elevated levels of Cd were predominantly in non-residual (extractable) fractions. The percentage of Cd in fractions decreased in the order of exchangeable > Fe-Mn oxide bound > residual > organic bound fraction. In contrast, soil Cu, Ni, Pb, and Zn were mainly in the residual (non-extractable) fractions. The fractions of Ni, Pb, and Zn followed the order of residual > Fe-Mn oxide bound > organic bound > exchangeable fraction; the fractions of Cu decreased in the order of residual > organic bound > Fe-Mn oxide bound fraction. The spatial distribution patterns of non-residual fractions exhibited similarities with the highest metal concentrations in the northwest area owing to the industries and E-waste recycling activities. Most metals in rice grain were the strongest spatially correlated with the exchangeable fraction, followed by the organic bound fraction, indicating that exchangeable and organic bound fractions had the highest bioavailability. Rice Cd and Zn were strong spatially correlated with exchangeable, Fe-Mn oxide, and organic bound fractions; rice Ni and Cu were strongly Responsible editor: Saulo Rodrigues Pereira Filho