Search

• PSTFS optimizes crop type classifications by fully mining crop phenology information. • PSTFS ranks features by importance and then iteratively prunes redundant features. • We used an SVM classifier to produce a high-quality corn map of Heilongjiang, China. • The optimal features identified by PSTFS are key crop phenological characteristics. • PSTFS can be extended to other land cover types and other dense remote sensing data. Accurate information on crop distribution and its changes is important for food security and environmental management. Although time series analysis is a widely-used and useful tool to characterize the seasonal dynamics of crops, the traditional image stacking approach misses important phenological events. This condition makes it difficult to identify the spectral and temporal features that are potentially important for crop identification, and therefore, makes it difficult to determine the optimal feature inputs for classifying crops with both high accuracy and low computation time. To address this gap, we developed a method to automatically select the spectro-temporal features by mining crop phenology information so as to improve the accuracy of crop classifications. This method of Phenology-based Spectral and Temporal Feature Selection (PSTFS) contains two major components: to identify the features with the highest separability between each pair of classes, and to prune redundant features to retain the best for classification. Using this optimal set of features and support vector machines (SVMs), we generated a high-quality corn cultivation map of China's Heilongjiang Province for 2011. The corn map had accuracies greater than 85% and agreed well with the corn census areas. We also demonstrate the goodness of this method for selecting features with high interpretability: it identified two phenological stages (three leaf and milky mature) that could best separate corn from other land use classes in the region. Our approach indicates the great potential for using the PSTFS method in conjunction with SVM classifiers to accurately map crop types based on satellite time series data. [ABSTRACT FROM AUTHOR]

Knowledge of cropping areas and climate change is crucial to understanding the causes and consequences of global land use change, and the response of rice areas to climate change is a hot topic to global food security. This study investigates the impacts of climate change on suitable areas for rice cultivation and how the actual cultivated area of rice has been altered in response to climate change during the past three decades. To understand whether the shifts in the extent and location of rice cropping areas match the pattern of climate change, the yearly climate data from 726 weather stations and the rice census data from 2,343 counties were employed to simulate the climatically suitable region for rice using the MaxEnt species distribution model, as well as to model the actual geographical distribution of rice using the spatial allocation production model in each decade. The results show that approximately 3.9 % of all Chinese land area (roughly 3.7 × 10 ha) has become suitable for rice due to climate change over the past three decades, representing new potential areas for rice cultivation. Meanwhile, the actual rice cropping area has increased by approximately 18.2 %, indicating that the extent and location of the rice expansion match the pattern of climate change. However, some spatial inconsistencies did exist between the actual rice area's expansion and the climatically suitable region after 1990. Nevertheless, climate change was a possible factor impacting the geospatial and temporal changes of the actual rice cropping area in China. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various research articles within the issue on topics including the impacts of climate changes on food production in China, the effect of temperature on yield of wheat and the relationship between climate change and cropland transition.

• A new methodological framework was proposed to develop improved scenarios. • Not all ecosystem services (ESs) increased with increasing spatial scales. • There were shifts in ES relationships between different urban hierarchical levels. • The influencing factors of ESs varied at different levels. • The scale-explicit land use scenarios can effectively enhance the priority ESs. Understanding how to manage ecosystem services (ESs) is crucial for sustainable regional development. The multiscale ES assessment can provide a new perspective for land use planning. We assessed the spatiotemporal patterns of multiple ESs in the three largest urban agglomerations of China at different levels: Beijing−Tianjin−Hebei, the Yangtze River Delta, and the Pearl River Delta from 1980 to 2018. The urban hierarchy extends from the city proper to metropolitan regions and then urban agglomeration levels. Associating with the trade-off relationships and influencing factors of ESs, a new methodological framework was proposed to enhance the ESs by developing improved land use scenarios at different urban hierarchical levels. The major results showed that: (1) the spatiotemporal patterns of ESs varied at different levels and not all ESs increased with increasing spatial scales; (2) the ES relationships at small scales did not apply to the broader scales and even led to diametrically opposing outcomes; (3) socioeconomic factors were the main drivers at the low level, whereas natural factors became the main drivers at the high level; and (4) more sustainable policy-combination scenarios were also place-based and scale-dependent, for example, strengthening intensive utilization of developed lands at the city proper level, implementing agroforestry intercropping at the metropolitan level, and facilitating forest conservation practices at the urban agglomeration level. It indicated that a multiscale approach should be considered when managing ESs. The scale-explicit land use strategies can help decision makers and planners to enhance the ESs and landscape sustainability more effectively and avoid misleading guidance. [Display omitted] [ABSTRACT FROM AUTHOR]

Climate change is now affecting global agriculture and food production worldwide. Nonetheless the direct link between climate change and food security at the national scale is poorly understood. Here we simulated the effect of climate change on food security in China using the CERES crop models and the IPCC SRES A2 and B2 scenarios including CO2 fertilization effect. Models took into account population size, urbanization rate, cropland area, cropping intensity and technology development. Our results predict that food crop yield will increase +3-11 % under A2 scenario and +4 % under B2 scenario during 2030-2050, despite disparities among individual crops. As a consequence China will be able to achieve a production of 572 and 615 MT in 2030, then 635 and 646 MT in 2050 under A2 and B2 scenarios, respectively. In 2030 the food security index (FSI) will drop from +24 % in 2009 to −4.5 % and +10.2 % under A2 and B2 scenarios, respectively. In 2050, however, the FSI is predicted to increase to +7.1 % and +20.0 % under A2 and B2 scenarios, respectively, but this increase will be achieved only with the projected decrease of Chinese population. We conclude that 1) the proposed food security index is a simple yet powerful tool for food security analysis; (2) yield growth rate is a much better indicator of food security than yield per se; and (3) climate change only has a moderate positive effect on food security as compared to other factors such as cropland area, population growth, socio-economic pathway and technology development. Relevant policy options and research topics are suggested accordingly. [ABSTRACT FROM AUTHOR]

To understand agro-climatic suitability for spring soybean growth in north China, an integrated crop-response-function method was developed. This method includes crop-response functions for temperature, precipitation, and sunshine and is assessed by a weighting method based on the coefficient of determination. The results show that the most suitable area (S1) for spring soybean growth occupied approximately 21.35%% of the total area of north China. Among three types of spring soybeans of early maturity, middle maturity, and late maturity, middle maturity was the most suitable variety to grow in the study area, covering nearly 1.133 ×× 106 km2 or about 99.75%% of the total area of S1. As a result of this study, the authors suggest that breeders pay more attention to middle-maturity cultivars in north China. The findings from this study may provide useful information for policy makers issuing guidelines for agricultural production. [ABSTRACT FROM AUTHOR]

Abstract: The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16kgha−1 lower than the RMSE (233.35kgha−1) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation. [Copyright &y& Elsevier]

Abstract: Loss of soil organic carbon (SOC) can cause soil degradation, which may not only undermine soil productivity, but may also-affect environmental health. In China, a huge amount of crop residues is regularly removed from the fields, and therefore China''s agriculture depends on high levels of chemical fertilizer inputs. This paper aims to estimate the SOC storage in Chinese cropland, identify its changing trends under current cropping systems, and finally put forward some strategies to keep the SOC in balance. A computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems (DeNitrification and DeComposition or DNDC) was applied to predict SOC dynamics in the upper (0–30 cm) soil layer of agricultural ecosystems at national scale. Data on climate, soil properties, cropping systems, acreage, and management practices at county scale were collected from various sources and integrated into a GIS database to support the model runs. The model results revealed (1) the total SOC storage in croplands in China is about 3968 Tg C; and (2) SOC is lost at a rate of 78.89 Tg C/year. The highest losses of SOC occur in the northeastern provinces. Chinese cropland soils release 186 Tg C as carbon dioxide into the atmosphere, and receive only 68 Tg C from crop residues annually. Considering the potential of global warming, SOC loss in cropland could be a serious contributor. Strategies to reduce the loss of SOC in Chinese cropland are proposed based on DNDC model runs for a number of scenarios under different management practices. [Copyright &y& Elsevier]

The intensive cropping system of "four-crops four-harvests" or "five-crops five-harvests" per year developed in Fugou County, Henan Province is a rotation system with very high productivity. The yield reached 15,825 kg/ha, which is 2.5 times of the yield per unit area in the traditional cropping systems of two crops per annum practiced in Henan Province. However, the concern has been whether these highly intensive agricultural practices are environmentally sustainable. This study attempts to address this concern by investigating the environmental, impact of the highly intensive farming system in Fugou County. Based on the production and economic results and analysis of key environmental indicators (soil fertility change, groundwater level change and content of N03-N in groundwater) over 12 years, we concluded that there is no significant resource depletion nor environmental degradation that occurred on the farm. However, more research should be oriented to the existing major problems and constraints affecting the highly intensive farming system since the data of the case study is still a short period of time. Finally, policy options for further improvement of the highly intensive farming are recommended. [ABSTRACT FROM AUTHOR]

In many countries, the governments support the development of local agriculture through subsidization. Subsidizing the sales of agricultural products through E-commerce channels is a way to support the development of agriculture in China. This study aims to develop a profit model and apply Stackelberg game theory to determine which type of subsidies and decision-making can provide the maximum benefits for agricultural products E-commerce supply chains. The results indicate that for both centralized decisions and decentralized decisions, the subsidizing to the agricultural cooperative is better than the subsidizing to consumers and no subsidization. The sales volume, preservation level, sales efforts, and overall profit of the agricultural products E-commerce supply chain are significantly higher. It suggests that the government should play a leading role to support the development of agricultural products E-commerce. This study contributes to agricultural research by developing a profit model to examine the effects of different government subsidy strategies on each member of the agricultural online shopping supply chain. Recommendations are provided for agricultural cooperatives, E-commerce platforms, and the government to improve the quality and sales of agricultural products through online shopping channels. [ABSTRACT FROM AUTHOR]

The impact of climate change on the crop water footprint (WF) is highly uncertain, hindering effective agricultural water use in response to climate change. This study proposes a factor sensitivity analysis method, which can screen out statistically significant climatic factors and interactions on crop WF under various uncertainties and determine water resource management measures to mitigate drought for different crops. A special case study was conducted in Heilongjiang Province, China. The results showed that (1) the annual change in crop WF showed a downward trend from 1988 to 2018. The crop WF was dominated by green water footprint (WF green), and the average occupancy rate of WF green in crops was 58.7%–74.1 %; the spatial distribution of WF has latitude zonality. (2) Drought has different effects on WF of different crops, and WF of soybean is susceptible to drought. Wind speed, sunshine hours, and humidity have a greater impact on crop WF in most growth stages. (3) The effect of climatic factors on crop WF varies in different months. The rice WF is mainly affected by the climate in May, and there is an interaction between May humidity and May rain. The WF of maize and soybeans are affected primarily by the climate in July, especially sunshine hours. The proposed approach attempts to analyze that crop WF is affected by not only an individual climatic factor but also their interactions. Crop water management practices should be adjusted based on the results to mitigate the adverse impact of climatic conditions on crop WF during different growing months. [ABSTRACT FROM AUTHOR]

Despite heated debates over the safety of genetically modified (GM) food, GM crops have been expanding rapidly. Much research has focused on the expansion of GM crops. However, the spatiotemporal dynamics of non-genetically modified (non-GM) crops are not clear, although they may have significant environmental and agronomic impacts and important policy implications. To understand the dynamics of non-GM crops and to inform the debates among relevant stakeholders, we conducted spatiotemporal analyses of China's major non-GM soybean production region, the Heilongjiang Province. Even though the total soybean planting area decreased from 2005 to 2010, surprisingly, there were hotspots of increase. The results also showed hotspots of loss as well as a large decline in the number and continuity of soybean plots. Since China is the largest non-GM soybean producer in the world, the decline of its major production region may signal the continual decline of global non-GM soybeans. [ABSTRACT FROM AUTHOR]

Challenges of ensuring food security under climate change require urgent and substantial increase in the focus of research, innovation, transformation of knowledge, and rapid adoption of available technologies. Here we simulate the effects of the adoption of higher-yielding varieties of rice, wheat and maize crops into the food production systems on China's food security index (FSI, or relative food surplus per capita) in 2050, using the CERES crop models, climate change and a range of socio- economic and agronomic scenarios which were developed following two contrasting development pathways in line with the IPCC A2 and B2 emission scenarios, respectively. The obtained results predict a slightly positive effect of climate change on the FSI, but the magnitude of this positive effect cannot compensate the negative effects of population growth, urbanization rate and the rising affluence on the future trends of the FSI. The outcomes of the adoption of higher-yielding varieties show that a systematic adoption of higher-yielding varieties can raise the average FSI values by a margin of 16 and 27 units under the A2 and B2 scenarios, respectively, during the 2030-2050 period, compared to the average predicted FSI values of -2 and 8 percentage points under A2 and B2 during the same period. This suggests that systematic adoption of higher-yield varieties is an effective measure for Chinese agriculture not only to ensure food security but also to build adaptive capacity to climate change in 2050. [ABSTRACT FROM AUTHOR]

Multiple cropping systems are particularly important in China to feed the 19% of the world’s population with only 8% of the arable land. Rising temperatures can dramatically affect multiple cropping systems and, as a consequence, food security in China. Here, we investigate the impacts of climate change on the northern limits and crop planting areas of multiple cropping systems in China, and estimate the impacts of the change in the crop planting areas of multiple cropping systems on the China’s crop production (maize, wheat, and rice). Based on both historical climate observations from the China Meteorological Administration and future climate A1B emission scenario (IPCC, 2007) data for China, we evaluate the effects of climate change on multiple cropping systems in China. Historical statistical crop yield and simulated crop yield by Agricultural Production Systems Simulator (APSIM model) in 2011–2100 were used to quantify the crop production (maize, wheat, and rice) in China. We found that the northern limits of multiple cropping systems have been shifted northward. The projected area of cultivated land for triple-cropping system may significantly expand during the 21st century. The northern shifts resulted in a 2.2% (∼8,000,000 t) increase in national production of three major crops (maize, wheat, and rice) during the period from 1981 to 2010, positively impacting China’s food security. Therefore, we conclude that the warming due to climate change may cause a positive impact on the crop production in China if concomitant changes adapted in multiple cropping systems take place. [ABSTRACT FROM AUTHOR]

Highlights: [•] We use survey data to analyze agricultural land system change in Northeast China. [•] Land system changes analyzed include land tenure, crop choice and intensification. [•] Spatial patterns of these aspects are related to road accessibility. [•] Crops for domestic use are increasingly replaced by cash crops in the study region. [•] Family characteristics affect land transfer while external drivers affect crop choice. [ABSTRACT FROM AUTHOR]

Abstract: Agricultural production plays an important role in affecting atmospheric greenhouse gas concentrations. Field measurements were conducted in Quzhou County, Hebei Province in the North China Plains to quantify carbon dioxide (CO2) and nitrous oxide (N2O) emissions from a winter wheat–maize rotation field, a common cropping system across the Chinese agricultural regions. The observed flux data in conjunction with the local climate, soil and management information were utilized to test a process-based model, Denitrification–Decomposition or DNDC, for its applicability for the cropping system. The validated DNDC was then used for predicting impacts of three management alternatives (i.e., no-till, increased crop residue incorporation and reduced fertilizer application rate) on CO2 and N2O emissions from the target field. Results from the simulations indicated that (1) CO2 emissions were significantly affected by temperature, initial SOC, tillage method, and quantity and quality of the organic matter added in the soils; (2) increases in temperature, initial SOC, total fertilizer N input, and manure amendment substantially increased N2O emissions; and (3) temperature, initial SOC, tillage, and quantity and quality of the organic matter added in the soil all had significant effects on global warming. Finally, five 50-year scenarios were simulated with DNDC to predict their long-term impacts on crop yield, soil C dynamics, nitrate leaching losses, and N2O emissions. The modelled results suggested that implementation of manure amendment or crop residue incorporation instead of increased fertilizer application rates would more efficiently mitigate GHG emissions from the tested agro-ecosystem. The multi-impacts provided a sound basis for comprehensive assessments on the management alternatives. [Copyright &y& Elsevier]

Reliable crop type maps are vital for agricultural monitoring, ensuring food security, and environmental sustainability assessments. Coarse-resolution imagery such as MODIS are widely used for crop type mapping due to their short revisit cycles, which is advantageous for detecting the seasonal dynamics of different crop types. However, the inherently low spatial resolution may restrict their utility for mapping crop types in regions with heterogeneous agricultural landscapes. Agricultural statistics, which provide crop acreage information at different spatial and temporal scales, have the potential to improve crop type mapping from remote sensing. Yet, previous studies have often used agricultural statistics as reference data to evaluate the accuracy of satellite-derived crop type maps but have rarely utilized them to improve crop type distribution mapping. The utility of integrating agricultural statistics with satellite images to produce high-accuracy crop type maps is rarely explored. This study presents a methodology for mapping sub-pixel crop type distributions via the integration of MODIS time series and agricultural statistics. We tested our approach in Heilongjiang Province, which has the highest agricultural production in China. First, we used an optimized random forest regression (RF-r) model with training samples derived from high spatial resolution images (i.e. SPOT and Landsat) to predict the sub-pixel crop type distributions from MODIS time series. To optimize the RF-r model, an 8-day MODIS time series of five vegetation indices in 2011 were used as the candidate independent variables, and a backward feature elimination strategy was implemented to select the best variables for model prediction. Second, we developed an Iterative Area Gap Spatial Allocation (IAGSA) method to spatially reconcile the discrepancies between the crop acreage estimated from MODIS-based maps and the agricultural statistics. We found that the MODIS-derived crop fractions agreed with those derived from the high-resolution images, with R2 > 0.75 for all crop types, yet there was a clear discrepancy between the crop acreage estimated from MODIS and agricultural statistics. The sub-pixel crop type maps adjusted by IAGSA were not only consistent with the agricultural statistics for crop acreage, but also retained the spatial distribution patterns of the original MODIS-derived crop fraction. Our results suggest the advantages of integrating coarse-resolution images and agricultural statistics to map sub-pixel crop type distributions, and to provide consistent estimation of crop acreage. The presented methodology has the potential to map large-scale crop type extent across regions in a cost-effective way. • Integrating MODIS images and agricultural statistics for crop type mapping. • Combing random forest with backward elimination strategy to select optimal features. • IAGSA reconciles the discrepancy between MODIS-based crop area and statistical data. • It is tested in Heilongjiang Province and generated high-quality crop fraction maps. • This method can be extended to other crop types and other coarse resolution images. [ABSTRACT FROM AUTHOR]

Up to date, most studies reported that degradation is worsened in the grassland ecosystems of Inner Mongolia and adjacent regions as a result of intensified grazing. This seems to be scientific when considering the total forage or total above-ground biomass as a degradation indicator, but it does not hold true in terms of soil organic carbon density (SOCD). In this study, we quantified the changes of grassland ecosystem carbon stock in Inner Mongolia and adjacent regions from the 1980s to 2000s and identified the major drivers influencing these variations, using the National Grassland Resource Inventory and Soil Survey Dataset in 1980s and the Inventory data during 2002 to 2009 covering 624 sampling plots concerned vegetal traits and edaphic properties across the study region. The result indicated that the above-, below-ground and total vegetation biomass declined from the 1980s to 2000s by ∼ 10 %. However, total forage production increased by 6.72 % when considering livestock intake. SOCD remained stable despite a 67 % increase in grazing intensity. A generalized linear model (GLIM) analysis suggested that an increase in grazing intensity from the 1980s to 2000s could only explain 1.04 % of the total biomass change, while changes in precipitation and temperature explained 17.7 % (p < 0.05) of total vegetation biomass (TVB) change. Meanwhile, SOCD change during 1980s - 2000s could be explained 10.08 % by the soil texture (p < 0.05) and <1.6 % by changes in climate and livestock. This implies that the impacts of climate change on grassland biomass are more significant than those of grazing utilization, and SOCD was resistant to both climate change and intensified grazing. Overall, intensified grazing did not result in significant negative impacts on the grassland carbon stocks in the study region during the 1980s and 2000s. The grassland ecosystems possess a mechanism to adjust their root-shoot ratio, enabling them to maintain resilience against grazing utilization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Taking Jining City of Shandong Province, one of the most important winter wheat production regions in Huanghuaihai Plain as an example, the winter wheat yield was estimated by using the 250 m MODIS-NDVI data smoothed by Savitzky-Golay filter. The NDVI values between 0. 20 and 0. 80 were selected, and the sum of NDVI value for each county was calculated to build its relation with winter wheat yield. By using stepwise regression method, the linear regression model between NDVI and winter wheat yield was established, with the precision validated by the ground survey data. The results showed that the relative error of predicted yield was between -3.6% and 3.9%, suggesting that the method was relatively accurate and feasible.