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Abstract 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.

AbstractOver the past decades, intensive reclamation has caused significant land degradation, threatening both ecological and food security. The Chinese government has taken a series of novel Conservation Tillage (CT) measures according to regional climate and soil conditions to increase agriculture’s multifunctional and resilience. Previous studies have reported CT in northeast China can improve soil organic carbon and reduce ammonia emissions in northeast China. However, whether the regionally adapted CT can avoid its inherent limitations in seed emergence and crop growth is still lacking comprehensive understanding and investigation. By using high-resolution and time-series Unmanned Aerial Vehicle (UAV) observations, our study investigated and evaluated the performance of different advanced and regional adapted CT treatments in crop emergence and growth compared with conventional tillage during the whole growing season. Based on the field measurements, we found that although the residues can impact the seed emergence, there was no significant difference between CT treatments and conventional tillage, with some CT treatments can even raise the emergence rate. We also found that the high residues covered CT treatments obstructed the maize grown in the early growing season including the V3 and V6 period, while the crop growth in these treatments caught up with the conventional tillage in the following growing periods. Our study suggested that well-adjusted CT treatments in Northeast China have the potential to maintain crop production accompanied by enhancing the ecosystem’s multifunctionality. The results from this study provided observational evidence of crop growth impact from CT, which will have great implications in convincing the farmers and beneficial to the promotion of conservation tillage.

The Heilongjiang-Amur River Basin is one of the largest and most complex aquatic systems in Asia, comprising diverse wetland resources. The wetland vegetation in mid-high latitude areas has high natural value and is sensitive to climate changes. In this study, we investigated the wetland vegetation cover changes and associated responses to climate change in the Heilongjiang-Amur River Basin from 2000 to 2018 based on the growing season (May to September) climate and LAI data. Our results indicated that the wetland LAI increased at 0.014 m2·m-2/yr across Heilongjiang-Amur River Basin with the regional climate showed wetting and warming trends. On a regional scale, wetland vegetation in China and Russia had positive partial correlation with solar radiation and minimum air temperature, with precipitation showing a slight lag effect. In contrast, wetland vegetation in Mongolia had positive partial correlation with precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found solar radiation and minimum air temperature are positively correlated with LAI, suggesting SR and minimum air temperature instead of mean air temperature and maximum air temperature play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with maximum air temperature in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited. Our investigation can provide a scientific foundation for the trilateral region in wetland ecosystem protection and is beneficial for a more comprehensive understanding of the responses of wetlands in the middle and high latitudes to climate change.

The Mongolian Plateau is an arid and semi-arid region with grassland as its main vegetation. It has a fragile ecosystem and is a sensitive area for global warming. The study is based on MODIS NDVI data and growth season meteorological data from 2000 to 2018, this study examined the spatial and temporal variation characteristics of grassland vegetation on the Mongolian Plateau during the growing season using trend analysis, partial correlation analysis, and residual analysis, and it explores the dual response of NDVI changes to climate and human activities. The study’s findings demonstrated that the growing season average NDVI of grassland vegetation on the plateau gradually increased from southwest to northeast during the growing season; the growing season average NDVI demonstrated a significant overall increase of 0.023/10a (p < 0.05) from 2000 to 2018, with an increase rate of 0.030/10a in Inner Mongolia and 0.019/10a in Mongolia; the area showing a significant increase in NDVI during the growing season accounted for 91.36% of the entire study area. In Mongolian Plateau grasslands during the growing season of 2000–2018, precipitation and downward surface shortwave radiation grew significantly at rates of 34.83mm/10a and 0.57 W/m2/10a, respectively, while average air temperature decreased slightly at a rate of −0.018°C/10a. Changes in meteorological factors of grassland vegetation varied by region as well, with Inner Mongolia seeing higher rates of precipitation, lower rates of average air temperature, and lower rates of downward surface shortwave radiation than Mongolia. On the Mongolian Plateau, the NDVI of grassland vegetation in the growing season showed a significant positive correlation with precipitation (0.31) and a significant negative correlation with average air temperature (−0.09) and downward surface shortwave radiation (−0.19), indicating that increased in NDVI was driven by an increase in precipitation paired with a decrease in air temperature and a decrease in surface shortwave radiation. The overall increase in NDVI caused by human activity in the grasslands of the Mongolian Plateau was primarily positive, with around 18.37% of the region being beneficial. Climate change and human activity both affect NDVI variations in Mongolian Plateau grasslands, which are spatially heterogeneous. Moderate ecological engineering and agricultural production activities are crucial for vegetation recovery. This work is crucial to further understanding surface–atmosphere interactions in arid and semi-arid regions in the context of global climate change.

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.