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Building a more resilient response system to climate change for sustainable development and reducing uncertainty in China's food markets, requires access to historical research gaps and mapping future research progress for decision making. However, the lack of quantitative and objective analyses to ensure the stability and development of agroecosystems increases the complexity of agro-climatic mechanisms, which leads to uncertainty and undesirable consequences. In this paper, we review the characteristics of climate change in China (1951–2020), reveal the mechanisms of agroecosystem structure in response to climate, and identify challenges and opportunities for future efforts in the context of research progress. The aim is to improve the scientific validity and relevance of future research by clarifying agro-climatic response mechanisms. The results show that surface temperature, precipitation, and frequency of extreme weather events have increased to varying degrees in major agricultural regions of China in 1951–2020. And they have strong geographic variation, which has resulted in droughts in the north and floods in the south. Moreover, climate change has complicated the mechanisms of soil moisture, Net Primary Productivity (NPP), soil carbon pool, and crop pest structure in agroecosystems. This lends to a reduction in soil water holding capacity, NPP, soil carbon content, and the number of natural enemies of diseases and insects, which in turn affects crop yields. However, human interventions can mitigate the deterioration of these factors. We have also realized that the methodology and theory of historical research poses a great challenge to future agroecosystem. Historical and projected climate trends identified current gaps in interdisciplinary integration and multidisciplinary research required to manage diverse spatio-temporal climate change impacts on agroecosystems. Future efforts should highlight integrated management and decision making, multidisciplinary big data coupling, and numerical simulations to ensure sustainable agricultural development, ecological security, and food security in China. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is an important viral disease, causing significant economic losses to the swine industry worldwide. Atypical cases caused by highly pathogenic PRRS virus (HP-PRRSV) emerged in 2006 in China. The vaccine strain HuN4-F112 has been developed from the wild-type HP-PRRSV HuN4 through repeated passages on MARC-145 cells. However, the mechanisms of attenuation have yet to be defined. Previous studies have shown that the vaccine strain HuN4-F112 could not effectively replicate in porcine alveolar macrophages (PAMs). In the present study, a series of chimeric and mutant PRRSVs were constructed to investigate regions associated with the virus attenuation. Firstly, the corresponding genome regions (ORF1a, ORF1b and ORFs 2-7) were exchanged between two infectious clones of HuN4 and HuN4-F112, and then the influence of small regions in ORF1a and ORF2-7 was evaluated, then influence of specific amino acids on NSP2 was tested. NSP2 was determined to be the key gene that regulated infection efficiency on PAMs, and amino acids at 893 and 979 of NSP2 were the key amino acids. The results of in vivo study indicated that NSP2 was not only important for infection efficiency in vitro, but also influenced the virulence, which was indicated by the results of survival rate, temperature, viremia, lung score and tissue score. [ABSTRACT FROM AUTHOR]

• Crop greening slightly decreased regional soil moisture but showed spatial variations. • Landscape pattern can affect how rainfall and soil moisture responds to crop greening. • Background precipitation increase masked the moisture deficit caused by crop greening. The discrepancy between water resource availability and agricultural production has become a dominant constraint on the sustainable development of agriculture. Recent satellite records have documented increasing vegetation growth since the 21st century, and this so-called "vegetation greening" has been unprecedented in the agricultural region of Northeast China. However, the influence of increased crop growth on regional water resources through the alteration of hydrological cycling remains unclear. This study investigated the impact of crop greening on the regional water balance in Northeast China using time-series satellite and observational records, machine learning, and numerical scenario simulations from WRF. Our results indicate that the enhanced water cycling during 2001–2020 in this region was characterized by a significant increasing trend in evapotranspiration, precipitation, and soil moisture. We found a complex sensitivity to the leaf area index (LAI) across Northeast China, with positive sensitivity in the southwestern regions and negative sensitivity in the typical black soil zones. The downwind precipitation induced by crop greening suggests that at the regional scale, the landscape pattern may play a more significant role in determining the water cycling response to crop greening. Our data-driven and model-driven results indicate that the background precipitation increase was the dominant contributor to the soil moisture increase, which masked the water deficit caused by crop greening. We also found that the continuous precipitation increasing trend projected by CMIP6 could further relieve conflicts between agriculture and water resources. In summary, our study provides insights into the impact of crop greening on the regional water balance in Northeast China and highlights the importance of considering both landscape patterns and background precipitation changes in understanding the water cycling response to crop greening. [ABSTRACT FROM AUTHOR]

[Display omitted] • The WRF coupled crop model was firstly calibrated and evaluated in Northeast China. • The localized crop model has robust performance in simulating crop dynamics. • The localized crop model has superiority in simulating crop activities and biomass. Northeast China is an important commodity grain base in China and plays an important role in maintaining national and local food security. The intensifying climate variability and strengthening of extreme weather events threaten crop growth and yields in this region. The dynamic crop model coupled into the land surface models showed advances in simulating crop phenology and crop growth response to climate change, however, which was mainly validated for the United States. By comparing with the satellite observed crop growth cycles, we firstly calibrated the parameters for the Weather Research and Forecasting (WRF) coupled Noah-MP-Crop model. Then, we evaluated the performance of the calibrated WRF-Noah-MP-Crop model with the default crop models and the dynamic vegetation models coupled with WRF by comparing our simulations with corresponding satellite measurements and meteorological observations. Our results indicated the augmentation of the Noah-MP-Crop model in WRF significantly improved the crop phenology simulation at the beginning and ending time of the growing season compared with the dynamic vegetation model using unmanaged crops. The satellite-calibrated crop parameters substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%. The coupled WRF-Noah-MP-Crop model with flexible resolutions holds unmatched advantages in estimating and projecting crop growth and yield under future climate change scenarios, which would be critical in ensuring sustainable agricultural development and maintaining future food security. [ABSTRACT FROM AUTHOR]

• Widespread greening cools surface and increases rainfall in summer over China. • Climatic responses to the greening showed significant regional variability. • Vegetation greening would decrease the risk of extreme climate events. Changes in land-use management and natural climate variability has led to significant vegetation greening in China over the past decades, as evidenced by satellite observations. In this study, we used a high-resolution land-atmosphere coupled regional climate model to assess the impacts of recent greening on China's regional summer climate. Our results inferred a country-averged surface cooling of 0.11 °C and a precipitation increase of 0.02 mm/d during summer in response to China's widespread greening. We also identified significant regional variability in the climatic responses to vegetation change. The regions of northern China showed significant cooling, including the Northeast China Plain, Loess Plateau, and the eastern areas of the Northern arid and semi-arid region; however, no significant temperature changes were simulated in southern China. Summer precipitation decreased in southern China and increased in the Huang-Huai-Hai Plain and the northern Loess Plateau. Variations in moisture flux convergence had a stronger influence on the precipitation patterns relative to evapotranspiration. Furthermore, our results suggested that vegetation greening would influence the occurrence of extreme climate events by decreasing the number of heatwave days (by 1–3 days, particularly in northern China), reducing the number of continuous drought events in northern China, and minimizing the risks of floods in southern China. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) has been increasingly recognized as having important effects on climate systems. Paddy fields, one kind of artificial wetland, have seen a significant increase in the Sanjiang Plain, China since 2000 and have become the most typical LUCC at the regional scale. Against this background, in this paper, we discuss the effects of this artificial wetland increase on surface temperature, in addition to its driving mechanisms. Firstly, the spatiotemporal variations of land surface temperature (LST) and its two driving variables (albedo and latent heat flux (LE)) in the Sanjiang Plain are analyzed and assessed based on remote sensing observation information from 2001 to 2015. Our results from both spatial distribution difference and time series analysis show that paddy field expansion led to day-time cooling and night-time warming over the study area. However, the LST changes show different characteristics and magnitudes in the spring (May to June) compared to the other months of the growing season (July to September). The daytime cooling trend is found to be −0.3842 K/year and the warming trend at night 0.1988 K/year during the period 2001 to 2015, resulting in an overall cooling effect in May and June. In July–September, the LST changes have the same sign but a smaller magnitude, with a −0.0686 K/year temperature trend seen for the day-time and a 0.0569 K/year increase for the night-time. As a consequence, a pronounced decrease in the diurnal temperature range is detected in the growing season, especially in spring. Furthermore, albedo and LE are demonstrated to be very sensitive to land use changes, especially in the earlier periods of the growing season. Correlation analysis between LST and albedo and LE also indicates the dominant role played by evapotranspiration in paddy fields in regulating local temperature. [ABSTRACT FROM AUTHOR]

Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale. [ABSTRACT FROM AUTHOR]

Diarrhea outbreaks in piglets on pig farms are commonly attributed to porcine epidemic diarrhea virus (PEDV) infection. This research analyzed the S gene prevalence variation and recombination patterns in PEDV GII strains. Throughout the previous two years, 172 clinical samples of piglet diarrhea have been collected, from which 24 PEDV isolates have been isolated. Analysis of the evolutionary relationships among all 24 S genes revealed that 21 were most closely related to strains within the GII-a subgroup. The 2 isolates grouped into one clade with the GII-b subgroup. According to the mutation analysis of the amino acids (aa) that encode the S protein, 43 of the common aa in strains of the GII subtype were found to have undergone a change in polarity or charge, and 36 of these aa had a mutation frequency of more than 90%. Three different aa mutation sites were identified as exclusive to GII-a subtype strains. The genomes of three PEDV isolates were sequenced, and the resulting range in genome length was 28,035−28,041 nt. The results of recombination analysis showed that the SD1 isolate is a novel strain recombinant from the foreign S-INDEL strain and a domestic GII subtype strain. Based on the findings, the PEDV GII-a strain has been the most circulating strain in several parts of China during the previous two years. Our study reveals for the first time the unique change of aa mutations in the S protein of the GII-a subtype strain and the new characteristics of the recombination of foreign strains and domestic GII subtype strains, indicating that it is crucial to monitor the epidemic dynamics of PEDV promptly to prevent and control the occurrence of PED effectively. [ABSTRACT FROM AUTHOR]

The Fifth IPCC Assessment Report indicates that climate change will affect crop growth and threaten the stability of food systems. Accumulated temperature, which is closely related to vegetation phenology and cropping systems, is an important indicator of heat in a region. Studying the history and future accumulated temperature changes can provide scientific reference for the change of crop phenology and cropping system, which is important for the improvement of grain production in China. Based on the MK trend test, MK abrupt change test and interpretable machine learning model, this study analyzes the spatial and temporal variation of accumulated temperature in China from 1979 to 2018, predicts its future variation based on CMIP6, and investigates the dominant influencing factors among different agricultural regions. The study found that (1) the accumulated temperature belt shows a northward shift and retreat trend toward higher altitudes, and the area of the high accumulated temperature belt increases year by year, leading to the narrowing of the area of the low accumulated temperature belt year by year, and the trend remains unchanged under the future scenario; meanwhile, the northward shift trend of the accumulated temperature belt is greatly mitigated and curbed under the SSP126 scenario. (2) The changes of accumulated temperature belt are mainly influenced by the increase of accumulated temperature duration days, and secondarily by the increase of temperature. The contribution brought by the first day of accumulated temperature from 1979 to 2018 is greater than that brought by the last day of accumulated temperature, while in the future scenario, on the contrary, changes in vegetation phenology delay should be given more attention. [ABSTRACT FROM AUTHOR]

The Northeast China is one of typical regions experiencing intensive human activities within short time worldwide. Particularly, as the significant changes of agriculture land and forest, typical characteristics of pattern and process of agroforestry ecotone change formed in recent decades. The intensive land use change of agroforestry ecotone has made significant change for regional land cover, which had significant impact on the regional climate system elements and the interactions among them. This paper took agroforestry ecotone of Nenjiang River Basin in China as study region and simulated temperature change based on land cover change from 1950s to 1978 and from 1978 to 2010. The analysis of temperature difference sensitivity to land cover change based on Weather Research and Forecasting (WRF) model showed that the land cover change from 1950s to 1978 induced warming effect over all the study area, including the change of grassland to agriculture land, grassland to deciduous broad-leaved forest, and deciduous broad-leaved forest to shrub land. The land cover change from 1978 to 2010 induced cooling effect over all the study area, including the change of deciduous broad-leaved forest to agriculture land, grassland to agriculture land, shrub land to agriculture land, and deciduous broad-leaved forest to grassland. In addition, the warming and cooling effect of land cover change was more significant in the region scale than specific land cover change area. [ABSTRACT FROM AUTHOR]

Understanding future climate change is crucial for securing and managing water supplies necessary for agricultural activities in China, particularly in rainfed agriculture regions. This paper evaluates the projected climate change for nine major agricultural zones in China, using ensembles of regional climate models (RCMs) simulated under the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II) framework. The simulations were conducted for two climate scenarios (RCP2.6 and RCP8.5) at a 25 km grid spacing, with three RCMs (RegCM4, REMO, and WRF) driven by multiple CMIP5 global climate models (GCMs). The study compares the RCM simulations to the driving CMIP5 and the recent CMIP6 ensembles, focusing on surface temperature, precipitation, and surface relative humidity for the present day (1981–2010), mid-future (2036–2065), and far future (2070–2099) periods. The model evaluations indicate that the GCM and RCM simulations are consistent with observations, with RCMs showing better performance than the driving GCMs. The results show that both GCMs and RCMs project increased temperature and precipitation in most agricultural zones of China in the future, with the CMIP6 ensembles projecting the largest temperature increase and the CORDEX-EA-II ensemble showing the finest spatial details. Relative humidity is projected to decrease over major rice-producing areas of South China in the CORDEX-EA-II, CMIP5, and CMIP6 ensembles. This study provides valuable information to support climate change adaptation and mitigation efforts in China, particularly in the agricultural sectors. • CORDEX-EA-II simulations demonstrate better model performance over China. • Temperature and precipitation are projected to increase in the future. • Relative humidity is projected to decrease over South China. [ABSTRACT FROM AUTHOR]

Wetland databases can provide the basic data that necessary for the protection and management of wetlands. A large number of wetland databases have been established in the world as well as in China. In this paper, we review China's wetland databases based on remote sensing (RS) technology after introducing the background theory to the application of RS technology in wetland surveys. A key conclusion is that China's wetland databases are far from sufficient in fulfilling protection and management needs. Our recommendations focus on the use of the hyper-spectral imagery, microwave data, multi-temporal images, and automatic classifications in order to improve the accuracy and efficiency of wetland inventory. Further, attention should also be paid to detect major biophysical features of wetlands and build wetland databases in years after the 1980s in China. Considering that great gap exists between RS experts and wetland experts, further cooperation between wetland scientists and RS scientists are needed to promote the application of RS in the foundation of wetland databases. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies. [ABSTRACT FROM AUTHOR]

The temperate forests in Northeast China are an important ecological barrier. However, the way in which temperate forests regulate the regional temperature and water cycling remains unclear. In this study, we quantitatively evaluated the role that temperate forests play in the regulation of the regional temperature and precipitation by combining remote sensing observations with a state-of-the-art regional climate model. Our results indicated that the forest ecosystem could slightly warm the annual air temperature by 0.04 ± 0.02 °C and bring more rainfall (17.49 ± 3.88 mm) over Northeast China. The temperature and precipitation modification function of forests varies across the seasons. If the trees were not there, our model suggests that the temperature across Northeast China would become much colder in the winter and spring, and much hotter in the summer than the observed climate. Interestingly, the temperature regulation from the forest ecosystem was detected in both forested regions and the adjacent agricultural areas, suggesting that the temperate forests in Northeast China cushion the air temperature by increasing the temperature in the winter and spring, and decreasing the temperature in the summer over the whole region. Our study also highlights the capacity of temperate forests to regulate regional water cycling in Northeast China. With high evapotranspiration, the forests could transfer sufficient moisture to the atmosphere. Combined with the associated moisture convergence, the temperate forests in Northeast China brought more rainfall in both forest and agricultural ecosystems. The increased rainfall was mainly concentrated in the spring and summer; these seasons accounted for 93.82% of the total increase in rainfall. These results imply that temperate forests make outstanding contributions to the maintainance of the sustainable development of agriculture in Northeast China. [ABSTRACT FROM AUTHOR]