The rice production system is one of the most climate change sensitive agro-ecosystems. This paper reviews the effects of current and future climate change on rice production in China. In recent decades, thermal resources have increased during the rice growing season, while solar radiation resources have decreased, and precipitation heterogeneity has increased. The increasing frequency of high-temperature stress, heavy rainfall, drought, and flood disasters may reduce the utilization efficiency of hydrothermal resources. Climate change, thus far, has resulted in a significant northward shift in the potential planting boundaries of single- and double-cropping rice production systems, which negatively affects the growth duration of single-, early-, and late-cropping rice. Studies based on statistical and process-based crop models show that climate change has affected rice production in China. The effects of climate change on the yield of single rice (SR), early rice (ER), and late rice (LR) were significant; however, the results of different methods and different rice growing areas were different to some extent. The trend of a longer growth period and higher yield of rice reflects the ability of China’s rice production system to adapt to climate change by adjusting planting regionalization and improving varieties and cultivation techniques. The results of the impact assessment under different climate scenarios indicated that the rice growth period would shorten and yield would decrease in the future. This means that climate change will seriously affect China’s rice production and food security. Further research requires a deeper understanding of abiotic stress physiology and its integration into ecophysiological models to reduce the uncertainty of impact assessment and expand the systematicness of impact assessment.

In recent years, heavy metals-induced soil pollution has increased due to the widespread usage of chromium (Cr) in chemical industries. The release of Cr into the environment has reached its peak causing hazardous environmental pollution. Heavy metal-induced soil pollution is one of the most important abiotic stress affecting the dynamic stages of plant growth and development. In severe cases, it can kill the plants and their derivatives and thereby pose a potential threat to human food safety. The chromium ion effect on plants varies and depends upon its severity range. It mainly impacts the numerous regular activities of the plant's life cycle, by hindering the germination of plant seeds, inhibiting the growth of hypocotyl and epicotyl parts of the plants, as well as damaging the chloroplast cell structures. In this review article, we tried to summarize the possible effects of chromium-induced stress on plant growth, developmental physiology, biochemistry, and molecular regulation and provided the important theoretical basis for selecting remedial plants in chromium-induced contaminated soils, breeding of low toxicity tolerant varieties, and analyzing the mechanism of plant resistance mechanisms in response to heavy metal stress.

Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest.

Based on an analysis of the current situation of nitrogen fertiliser application, it is suggested that improving the nitrogen utilisation efficiency of crops is an important means of promoting the sustainable development of agriculture and realises the zero increase in chemical fertiliser application. Nitrate loss and nitrous oxide (N₂O) emissions caused by nitrification and denitrification are the main reasons for the low utilisation rate of nitrogen fertilisers. N₂O is a greenhouse gas that has caused a sharp increase in global temperature. Biological nitrification inhibition refers to releasing natural compounds that inhibit nitrification from plant roots. The natural compounds released are called biological nitrification inhibitors (BNIs), which specifically inhibit the activity of microorganisms in soil nitrification. Biological nitrification inhibitors can significantly improve rice (Oryza sativa), corn (Zea mays) and other crops by 5–10%, which can increase the nitrogen utilisation rate of corn by 3.1%, and reduce greenhouse gas N₂O emissions. Compared with plants that do not produce BNI, the amount of N₂O released can be reduced by up to 90%. The BNI released by Brachialactone (Brachiaria humidicola) accounted for 60–90% of the total inhibition of nitrification. In summary, biological nitrification inhibitors that inhibit nitrification, improve nitrogen utilisation and crop yield, and reduce greenhouse gas emissions play an important role. This paper reviews the plants known to release BNIs, reviews the plants known to inhibit soil nitrification but with unknown BNIs and further discusses the important role of bio nitrification inhibition in agricultural systems.