Spatial-temporal changes of cropland and climate potential productivity in northern China during 1990–2010

Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

This study employs an improved Carnegie-Ames-Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Natural capital investments in China undermined by reclamation for cropland

Globally, rising food demand has caused widespread biodiversity and ecosystem services loss, prompting growing efforts in ecological protection and restoration. However, these efforts have been significantly undercut by further reclamation for cropland. Focusing on China, the world's largest grain producer, we found that at the national level from 2000 to 2015, reclamation for cropland undermined gains in wildlife habitat and the ecosystem services of water retention, sandstorm prevention, carbon sequestration and soil retention by 113.8%, 63.4%, 52.5%, 29.0% and 10.2%, respectively. To achieve global sustainability goals, conflicts between inefficient reclamation for cropland and natural capital investment need to be alleviated.

Spatiotemporal variations and driving factors of global terrestrial vegetation productivity gap under the changing of climate, CO2, landcover and N deposition

Understanding the gap between potential productivity and the actual productivity of vegetation (vegetation productivity gap, VPG) is the basis to explore the potential productivity improvement and identify its constraints. In this study, we used the classification and regression tree model to simulate the potential net primary productivity (PNPP) based on the flux-observational maximum net primary productivity (NPP) across different vegetation types, that is, potential productivity. The actual NPP (ANPP) is obtained from the grid NPP averaged over five terrestrial biosphere models, and the VPG is subsequently calculated. On this basis, we used the variance decomposition method to separate the effects of climate change, land-use change, CO₂, and nitrogen deposition on the trend and the interannual variability (IAV) of VPG from 1981 to 2010. Meanwhile, the spatiotemporal variation characteristics and influencing factors of VPG under future climate scenarios are analyzed. The results showed an increasing trend in PNPP and ANPP, while there was a decreasing trend of VPG in most parts of the world and this trend is more significant under representative concentration pathways (RCPs). The turning points (TP) of VPG variation are found under RCPs and the reduction trend of VPG before TP is more than that after TP. The VPG reduction in most regions was caused by the combined effects of PNPP and ANPP (41.68 %) from 1981 to 2010. However, the dominant factors of global VPG reduction are changing under RCPs, and the increment of NPP (39.71 % - 49.3 %) has become the dominating factor of VPG variation. CO₂ plays a decisive role in the multi-year trend of VPG, while climate change is the main factor determining the IAV of VPG. Under changing climate, temperature and precipitation are negatively correlated with VPG in most parts of the world, and the relationship between radiation and VPG from weak negative to positive correlation.

Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin

Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R²), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.

Analysis and Functional Verification of PlPM19L Gene Associated with Drought-Resistance in Paeonia lactiflora Pall

The herbaceous peony (Paeonia lactiflora Pall.) is widely cultivated as an ornamental, medicinal and edible plant in China. Drought stress can seriously affect the growth of herbaceous peony and reduce its quality. In our previous research, a significantly differentially expressed gene, PM19L, was obtained in herbaceous peony under drought stress based on transcriptome analysis, but little is known about its function. In this study, the first PM19L that was isolated in herbaceous peony was comprised of 910 bp, and was designated as PlPM19L (OP480984). It had a complete open reading frame of 537 bp and encoded a 178-amino acid protein with a molecular weight of 18.95 kDa, which was located in the membrane. When PlPM19L was transferred into tobacco, the transgenic plants had enhanced tolerance to drought stress, potentially due to the increase in the abscisic acid (ABA) content and the reduction in the level of hydrogen peroxide (H₂O₂). In addition, the enhanced ability to scavenge H₂O₂ under drought stress led to improvements in the enzyme activity and the potential photosynthetic capacity. These results combined suggest that PlPM19L is a key factor to conferring drought stress tolerance in herbaceous peony and provide a scientific theoretical basis for the following improvement in the drought resistance of herbaceous peony and other plants through genetic engineering technology.

Understanding impacts of cropland pattern dynamics on grain production in China: A integrated analysis by fusing statistical data and satellite-observed data

Detailed information on spatial distribution of croplands and grain yields is crucial for agricultural management and food security, but is often limited by a lack of geospatial data. By integrating satellite observation and statistical data, this study produced new geospatial data of cropland areas and grain yields in China during 2000-2020. We found that the decrease of relatively high-yielding croplands in southern China mainly caused by the expansion of constructed land. Yet, the increase of croplands largely occurred in temperature/water-limited regions of Northern Arid and Semiarid Region (NASR) and Northeast China Plain (NCP). Croplands' decrease in southern China and expansion in NCP and NASR jointly led to the continuous northward shift of the centre of gravity of croplands and grain yields. This spatial transfer of croplands resulted in relatively lower-than- average grain yield per unit area (AGYA) croplands decreasing from 38.96% (2000) to 35.75% (2020), but also relatively higher-than-AGYA croplands decreasing from 38.41% (2000) to 35.01% (2020), implying spatial challenges of grain production. Generally, every 1 km² of cropland loss in traditional high-yield zones required nearly 1-3 times expansion in area in NASR and NCP to balance grain yield losses. The new geospatial data and these findings from this study could provide valuable information for regional agriculture development and policy marking.

Multiscale analysis of factors affecting food security in China, 1980-2017

Food security is an important issue affecting people's lives and social stability. Clarifying levels of food security and the factors affecting it (social, economic, agricultural, climatic) can help improve regional food security. The spatiotemporal patterns and driving factors of food security vary at different scales. There is, however, a lack of research that considers the various factors affecting food security at multiple scales. This study, therefore, analyzed dynamic spatiotemporal changes in food security at small (city), medium (province), and large (country) scales; identified hot and cold areas of food security; and revealed the main factors affecting food security at different scales. A food security index (FSI) was built based on the coupling of grain yield, population, and GDP, and spatial analysis was used to evaluate dynamic spatiotemporal changes in China's food security from 1980 to 2017. Further, the relationship between food security and its driving factors was quantitatively analyzed using stepwise regression. The results showed greater heterogeneity in food security at the smaller scale than at the larger scale. The key factors affecting food security varied substantially at different scales: the added value of tertiary industry dominated the prefecture level, and gross agricultural output value was the main factor at the provincial and national levels. Multiple-scale research can reveal the status and primary factors of food security and provide a decision-making basis for improving regional food security.

Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change

Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980-2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m². The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Chinese croplands have changed considerably over the past decades, but their impacts on the environment remain underexplored. Meanwhile, understanding the contributions of human activities to vegetation greenness has been attracting more attention but still needs to be improved. To address both issues, this study explored vegetation greening and its relationships with Chinese cropland changes and climate. Greenness trends were first identified from the normalized difference vegetation index and leaf area index from 1982–2015 using three trend detection algorithms. Boosted regression trees were then performed to explore underlying relationships between vegetation greening and cropland and climate predictors. The results showed the widespread greening in Chinese croplands but large discrepancies in greenness trends characterized by different metrics. Annual greenness trends in most Chinese croplands were more likely nonlinearly associated with climate compared with cropland changes, while cropland percentage only predominantly contributed to vegetation greening in the Sichuan Basin and its surrounding regions with leaf area index data and, in the Northeast China Plain, with vegetation index data. Results highlight both the differences in vegetation greenness using different indicators and further impacts on the nonlinear relationships with cropland and climate, which have been largely ignored in previous studies.

Accelerating Cities in an Unsustainable Landscape: Urban Expansion and Cropland Occupation in China, 1990–2030

It is crucial to assess the effects of urban expansion on croplands to allow sustainable urbanization and cropland supply. However, owing to the complexity of land conversion and various land policies in China, it is difficult to quantify the cropland dynamics and implications of urban expansion throughout the whole accelerated stage of urbanization. This study was based on land use data from 1990 to 2015 and urban expansion data from 2000 to 2030, analyzing urban expansion and predicting its impact on croplands. We found that urban area would continue to increase and croplands would contribute more than 70% of the urban expansion area. The urban area in China will likely reach 71.6–87.0 thousand km2 or more by 2030. Although the overall area of croplands may remain at a similar magnitude in future decades, our findings imply that croplands will tend to shift northward, resulting in some potential challenges owing to resource limitations in northern regions. Our study provides a new perspective in terms of assessing future cropland dynamics and the effects of urban expansion and highlights the significance of ensuring a realistic land policy in the future.

Spatio-Temporal Dynamics of Maize Potential Yield and Yield Gaps in Northeast China from 1990 to 2015

Maize yield has undergone obvious spatial and temporal changes in recent decades in Northeast China. Understanding how maize potential yield has changed over the past few decades and how large the gaps between potential and actual maize yields are is essential for increasing maize yield to meet increased food demand in Northeast China. In this study, the spatial and temporal dynamics of maize potential yield in Northeast China from 1990 to 2015 were simulated using the Global Agro-ecological Zones (GAEZ) model at the pixel level firstly. Then, the yield gaps between actual and potential yields were analyzed at city scale. The results were the following. (1) The maize potential yield decreased by about 500 kg/ha and the potential production remained at around 260 million tonnes during 1990–2000. From 2000 to 2015, the maize potential yield and production increased by approximately 1000 kg/ha and 80 million tonnes, respectively. (2) The maize potential yield decreased in most regions of Northeast China in the first decade, such as the center area (CA), south area (SA), southwest area (SWA), and small regions in northeast area (NEA), due to lower temperature and insufficient rainfall. The maize potential yield increased elsewhere. (3) The maize potential yield increased by more than 1000 kg/ha in the center area (CA) in the latter 15 years, which may be because of the climate warming and sufficient precipitation. The maize potential yield decreased elsewhere and Harbin in the center area (CA). (4) In 40 cities of Northeast China, the rates of actual yield to potential yield in 17 cities were higher than 80%. The actual yields only attained 50–80% of the potential yields in 20 cities. The gaps between actual and potential yields in Hegang and Dandong were very large, which need to be shrunk urgently. The results highlight the importance of coping with climate change actively, arranging crop structure reasonably, improving farmland use efficiency and ensuring food security in Northeast China.