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Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b). [ABSTRACT FROM AUTHOR]

Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R2 of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R2 of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R2 of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops. [ABSTRACT FROM AUTHOR]

• The increasing trend of vapor pressure deficit (VPD) change in 1997–2020 is ten times higher than in 1981–1996. • Increasing VPD promoted cropland and grassland growth in Arid, while inhibited the growth of shrubland. • Significant positive or negative difference in the sensitivity to VPD for the same vegetation under different climatic zones. • The vast majority of vegetation showed a significant increase in the inter-annual trend of the sensitivity to VPD. • The relationship of higher sensitivity with lower VPD is not stable under different vegetation and climatic zones. Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis. [ABSTRACT FROM AUTHOR]

In shallow lakes, wind wave turbulence alters underwater spectral composition, but the influence of this phenomenon on phytoplankton community structure is poorly understood. We used 100L mesocosms to investigate the influence of light quality on a natural phytoplankton community collected from Taihu Lake in China. The communities in mesocosms were exposed to sunlight filtered for white, blue, green, and red light, while wave-making pumps simulated wind wave turbulence similar to Taihu Lake. Over the course of experiment, each filtered light reduced the total phytoplankton abundance compared to white light. The mean abundance of phytoplankton in controls was 1.72, 1.78, and 7.89 times of that in the red, blue, and green light treatments. Red, blue, and green light significantly promoted the growth of cyanobacteria, green algae, and diatoms, respectively, and induced successional change of the phytoplankton species under the tested conditions. The proportion of Microcystis to total phytoplankton abundance in controls and red light shifted from 87.09% at the beginning to 37.95% and 56.30% at the end of the experiment, respectively, and maintained its dominance, whereas Microcystis lost its dominance and was replaced by Scenedesmus (53.78%) and Synedra (53.18%) in the blue and green light, respectively. Given the process of how these phytoplankton compete in designated spectrum, exploring these influences could help provide new insights into the dominance formation of toxic cyanobacteria. [ABSTRACT FROM AUTHOR]

• Accurately forecast the first flowering period of two typical apple producing areas in China. • It fills in the research of forecasting the first flowering period of apple from point scale to regional scale. • The phenological survey method of replacing time with space is very effective for the calibration of sequential model. • The prediction of the apple first flowering on a regional scale has important application value for optimizing orchard management. China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management. [ABSTRACT FROM AUTHOR]

• Proposed a tea plantation phenological feature index (TPI). • Constructed a mapping algorithm based on multitemporal spectral features. • Tea plantations were accurately distinguished from evergreen vegetation. • The tea plantation mapping algorithm is temporally reusable. Tea is a highly demanded and valuable commodity in both domestic and international markets, with China being the world's largest producer and exporter, resulting in the expansion of tea tree cultivation in China over the past few decades. Accurate mapping of tea plantations is vital for tea markets, food security, poverty alleviation, and ecosystem value assessment. However, research on vegetation plantation monitoring has focused primarily on food crops, and the spatial information for the perennial tea tree is often neglected. Moreover, monitoring tea plantations poses significant challenges in terms of the diverse cultivation management measures, the heterogeneity of tea garden landscapes, and the limited availability of clear imagery due to cloud cover. In this study, we constructed a novel tea plantation mapping algorithm based on multitemporal spectral features. In addition, a Tea Plantation Phenological Feature Index (TPI) is proposed to enable easy and reliable monitoring of tea plantations. The algorithm harmonized the Landsat7/8 and Sentinel-2 satellite images on the Google Earth Engine (GEE) platform, utilizing a semi-automatic decision tree model to identify tea gardens in Yingde City, Guangdong Province. Our results show that the tea plantation phenological feature index (TPI), green band in October (Green Oct) and cumulative band in April (CB Apr) are the most effective multitemporal spectral features for identifying tea gardens. The F1-score, OA, and kappa coefficients (Kappa) of the 2020 tea plantation map generated by the tea plantation mapping algorithm based on multitemporal spectral features are 90.75%, 92.71%, and 0.844, respectively. Furthermore, the algorithm is time-reusable, as evidenced by the F1-score, OA, and kappa coefficients of the generated 2021 tea plantation map, which are 85.99%, 88.86%, and 0.7675, respectively. Our results underscore the exceptional ability of the multitemporal spectral features to separate tea gardens from evergreen forests. The tea plantation mapping algorithm based on multitemporal spectral features is a robust, user-friendly, and effective tool suitable for regional or national annual tea plantation mapping, enabling sustainable production and implementing. [ABSTRACT FROM AUTHOR]

In recent years, porcine epidemic diarrhea (PED) has become widespread and caused huge economic losses for the global pig industry. There is growing evidence that frequent outbreaks of diarrhea are caused by the variants of porcine epidemic diarrhea virus (PEDV) with high pathogenicity. Herein, an epidemic strain of PEDV HLJ strain was isolated and characterized from Heilongjiang Province of China, and the whole genomic expression profile of intestinal porcine epithelial cells (IPEC-J2) infected with HLJ strain was investigated in comparison with classical CV777 strain. A total of 26,851 genes were identified, of these, 25,880 were known genes and 971 were novel genes. There were 258 differentially expressed genes (DEGs) identified between PEDV HLJ-infected and uninfected cells at 24 h post infection (hpi), and 201 DEGs between PEDV HLJ and CV777 infection. A comparative analysis revealed that 258 DEGs were enriched in 468 gene ontology (GO) terms and mapped to 179 KEGG pathways, and 201 DEGs in 1120 GO terms and mapped to 115 KEGG pathways for HLJ-infected cells in contrast to the uninfected and CV777-infected cells, respectively. Specifically, PEDV HLJ strain could activate anti-viral innate immune response and inflammation more intensively than CV777, in which mRNA levels of interferon (IFN-β), chemokines (CCL5 and CXCL10) and pro-inflammatory cytokines (IL-8 and TNF-α) were induced earlier and more strongly. Subsequently, 20 DEGs and 5 proteins were selected and validated by real-time fluorescence quantitative PCR (RT-qPCR) and western blot, and the results were consistent with the transcriptomic analysis. Overall, this study may be helpful for understanding the pathogenesis mechanism of PEDV variants, and contribute to the effective prevention and control of PEDV infection. • PEDV HLJ strain could adapt and propagate on IPEC-J2 cells with a highest titer of 105.6 TCID 50 at 48 h post infection after serial passages. • The epidemic PEDV HLJ strain, as classified into GI b subgroup, has obvious mutations in its spike and ORF3 protein alignment. • Comparative transcriptomic analysis showed PEDV HLJ strain infection could trigger earlier and stronger responses of inflammation, apoptosis, and innate immunity, in comparison with CV777 strain in cell mitosis and metabolism. This will provide new clues for the further study of the pathogenesis mechanism and PED prevention. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-source environmental data were used to predict wheat yield in China. • We developed a robust machine learning model to predict wheat yield. • The random forest model using NIRv had better performance than models using NDVI and EVI. • NIRv from jointing to heading was the most important predictor in determining yield. Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering–heading), relative humidity (during planting–tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data. [ABSTRACT FROM AUTHOR]

Timely and accurate forecasting of crop grain protein content (GPC) is helpful in planning to acquire the desired target protein levels. A geographically weighted regression (GWR) model was estimated based on meteorological factors to predict the winter wheat GPC at the county level. In the Huang-Huai-Hai region, the grain protein content of winter wheat increased by 0.29% for every 1° increase in latitude. GPC prediction with this model was more precise than that of the multiple linear regressions (MLR) model. The correlation coefficient (R) and Akaike information criterion (AIC) value ranges were 0.26 ~ 0.66 and 1573.86 ~ 1710.70 for the GWR, and 0.06 ~ 0.46 and 1670.18 ~ 1939.76 for the MLR, respectively. Except for radiation in March (RAD03), radiation in April (RAD04) and radiation in May (RAD05), the sensitivity index of other monthly weather indicators to GPC had a high correlation with latitude. With 36° north latitude (L) as the limit, the correlation between RAD03 (R L<36 ° = 0.36, R L>36 ° = −0.29), RAD04 (R L<36 ° = 0.31, R L>36 ° = −0.35) and RAD05 (R L<36 ° = 0.20, R L>36 ° = −0.20) with latitude all showed an opposite trend. We highlight that spatial information needs to be considered when predicting county-level winter wheat GPC. • The grain protein content (GPC) is positively correlated with latitude. • The effect of meteorological factors on winter GPC showed spatial heterogeneity. • Geographically weighted model has advantages in winter GPC in heterogeneous region. [ABSTRACT FROM AUTHOR]

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context. [Display omitted] • 21 barriers to Chinese sustainable apple production were identified. • Vicious circles trap different problems in the apple production system. • There exists socio-technical lock-in around existing tools and practices. • Ineffective agricultural policies strongly hinder upgrading apple production. • A three-stage approach was established for researching agricultural sustainability. [ABSTRACT FROM AUTHOR]

• Develop a within-growing season yield forecast system with random forest model. • Random forest model performs well in predicting grain yield in China. • We identified the most important stage-specific predictors determining crop yield. • The most important variable influencing yields varied with crop types. Accurate and timely crop yield forecasts can provide essential information to make conclusive agricultural policies and to conduct investments. Recent studies have used different machine learning techniques to develop such yield forecast systems for single crops at regional scales. However, no study has used multiple sources of environmental predictors (climate, soil, and vegetation) to forecast yields for three major crops in China. In this study, we adopted 7-year observed crop yield data (2013–2019) for three major grain crops (wheat, maize, and rice) across China, and three major data sets including climate, vegetation indices, and soil properties were used to develop a dynamic yield forecasting system based on the random forest (RF) model. The RF model showed good performance for estimating yields of all three crops with correlation coefficient (r) higher than 0.75 and normalized root means square errors (nRMSE) lower than 18.0%. Our results also showed that crop yields can be satisfactorily forecasted at one to three months prior to harvest. The optimum lead time for yield forecasting depended on crop types. In addition, we found the major predictors influencing crop yield varied between crops. In general, solar radiation and vegetation indices (especially during jointing to milk development stages) were identified as the main predictor for winter wheat; vegetation indices (throughout the growing season) and drought (especially during emergence to tasseling stages) were the most important predictors for spring maize; soil moisture (throughout the growing season) was the dominant predictor for summer maize, late rice, and mid rice; precipitation (especially during booting to heading stages) was the main predictor for early rice. Our study provides insights into practical crop yield forecasting and the understanding of yield response to environmental conditions at a large scale across China. The methods undertaken in this research can be easily implemented in other countries with available information on climate, soil, and vegetation conditions. [ABSTRACT FROM AUTHOR]

Light and hydrodynamic force are important physical factors affecting growth of Microcystis. The most recent study found that green light has good effect in inhibiting growth of Microcystis. To understand the effect of mixing modes on Microcystis under the green light, we investigated the effects of continuous mixing and intermittent mixing on the abundance of Microcystis in Taihu Lake under field conditions. The study results found that abundance of Microcystis in control, intermittent mixing group, and continuous mixing group decreased 76.62%, 40.36%, and 95.18% on day 7 compared with that on day 1 in this experiment. At the end of the experiment, abundance percentages of diatoms and green algae to total phytoplankton abundance were 1.57% and 0.48% in control, 2.32% and 0.67% in intermittent mixing group, and 22.47% and 20.27% in continuous mixing group. The results indicated that continuous mixing favored the removal of Microcystis under green light conditions and was helpful for the growth of green algae and diatoms. The results provide a new approach for the control of Microcystis blooms., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Trace element is one of the important factors affecting the growth of Microcystis. The effects of zinc (0.4 mg/L) and iron (2 mg/L) on the abundance of Microcystis in Lake Taihu were investigated under continuous turbulence and green light conditions in a microcosm experiment. The study results showed that the abundance of Microcystis in the zinc treatment and the iron treatment group was 8.30% and 214% of that in the control group at the end of the experiment, respectively. The proportion of Cyanobacteria in the total phytoplankton biomass in the control, iron treatment, and zinc treatment group decreased from 99.99% at the beginning of the experiment to 13%, 18%, and 1% at the end of the experiment, respectively. At the end of the microcosm experiment, the phytoplankton community was dominated by Bacillariophyta in the control group, accounting for 63%, but it was dominated by Chlorophyta in the zinc treatment and the iron treatment group, accounting for 89% and 42%, respectively. The study results showed that under green light and turbulence, 0.4 mg/L zinc remarkably decreased the abundance of Microcystis, but 2 mg/L iron effectively increased the number of Microcystis and other algae. This research results provided a new idea for controlling Microcystis blooms., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)