Maps of cropping patterns in China during 2015-2021

Agricultural machinery could contribute 20% of total carbon and air pollutant emissions by 2050 and compromise carbon neutrality targets in China

Agricultural mechanization has benefitted food security in China, but carbon dioxide (CO2) and air pollutant emissions from fuel combustion are often overlooked. Here we show that emissions of CO2 and air pollutants from agricultural machinery increased nearly sevenfold and four- to sevenfold, respectively, during 1985-2020, driven largely by rapid advancement in the mechanization level. If unabated, annual emissions of CO2, PM2.5 and NOx from agricultural machinery in 2050 could reach 213.6 Mt, 55.4 Gg and 902.8 Gg, contributing ~21%, ~4% and ~17% of China's total emissions under a dual-carbon goal scenario, respectively. However, adoption of renewable energy sources could mitigate 65-70% of these emissions. Our study highlights that China's agricultural machinery could become a large source of emissions that-without mitigation-may hinder China's carbon neutrality targets and degrade air quality.

High-resolution mapping of China's flooded croplands

Climate change and the increasing frequency of floods have undermined China's food security. Creating detailed maps of flooded croplands is essential to improve prevention and adopt effective adaptation initiatives. Previous large-scale flood mapping efforts were hampered by limited meteorological and hydrological data, and the susceptibility of optical satellite images to cloud cover, leading to high uncertainty when downscaled to the cropland-scale. Here, using 4968 near-real-time (NRT) Sentinel-1 SAR (S1) images (spatial resolution: 10 m), we generated China's first set of high-resolution flooded cropland maps covering the period from 2017 to 2021. Our results demonstrate that croplands accounted for 43.8% to 49.8% of China's total flooded areas (ranging from 82,175 km2 to 122,037 km2). We also created high-resolution flood maps specifically for rice and maize crops. The inundated rice areas ranged from 8428 km2 to 22,123 km2, accounting for 22.34% to 41.91% of the annual flooded croplands, or 2.82% to 7.45% of the annual rice cropland. In comparison, the inundated maize cropland fluctuated from 2619 km2 to 5397 km2, representing 5.38% to 13.56% of the annual flooded croplands. Our findings revealed extensive floods in rural areas, highlighting the urgent need to prioritize flood prevention and mitigation efforts in such regions. In light of China's allocation of an additional 1-trillion-RMB treasury bonds for water infrastructure projects, the high-resolution flood maps can be used to select sites for flood control projects, and evaluate the impact of flooding on crop yields and food security, thus targeting poverty alleviation in rural areas of China.

Pesticide dynamics and risk assessment in a plateau lake: Multiphase partitioning, drivers, and distribution in Southwestern China

Erhai Lake, a vital drinking water source for Dali, a highland agricultural city, faces potential contamination from pesticide residues, yet limited studies have assessed their distribution and impacts. This study investigates the occurrence, transport, partitioning, and ecological risks of pesticides in the lake's dissolved phase (DP), suspended particulate matter (SPM), and sediment (SD) samples collected from 22 sites across different seasons. The results showed significant temporal variations across different media, with spatial variations driven by crop-related patterns. Atrazine, etridiazole, and cis-permethrin were identified as the most abundant pesticides in DP, SPM, and SD, respectively. Notably, the source-sink dynamics were not only driven by deposition and resuspension but influenced by multiple sources and hydrodynamic processes such as precipitation, phytoplankton biomass, organic carbon, and winds. Ecotoxicological assessments indicated that permethrin, endrin, and endosulfan sulfate posed significant ecological risks to aquatic organisms. Although human health risks from pesticides were low, ongoing monitoring of atrazine is recommended due to its extensive use around Dali City.

Enhanced Soil Emissions of Reactive Nitrogen Gases by Fertilization and Their Impacts on Secondary Air Pollution in Eastern China

Nitrogen fertilizer application is accompanied by intense release of multiple reactive nitrogen (Nr) gases such as nitrous acid (HONO), ammonia (NH3), and nitric oxide (NO) from the soil, influencing atmospheric chemistry and air pollution. In current emission inventories, postfertilization soil emissions are poorly characterized due to inaccurate identification of fertilization timing and location. Moreover, pre-existing studies predominantly focus on individual Nr gases, and a comprehensive understanding of simultaneously emitted Nr gases from fertilization and their impacts on air quality is still limited. Here, we developed a novel method to identify the dryland fertilization activity based on satellite and reanalysis data sets. Then, we updated a dynamic soil Nr emissions model (WRF-SoilN-Chem) with lab-derived parametrization and applied it to analyze the time- and space-varying Nr emissions and their effects on air quality. It is estimated that the Nr emissions from a typical fertilization event in the Yangtze River Delta (YRD) region increased ozone (O3) and nitrate concentrations by 2.5 and 18.2%, respectively. HONO and NH3 emissions jointly enhanced nitrate production via gas-particle partitioning. An accurate representation of fertilization and meteorology-emission-chemistry coupled modeling would greatly improve the understanding of the soil Nr emissions and their impacts on regional air pollution.

A comprehensive map of DNA-segment copy number variation in 491 genomes of common wheat uncovers genes associated with multiple agronomic traits

DNA-segment copy number variations (DSCNVs), such as deletions and duplications, are important sources of genomic structural variation. However, the types and sizes of DSCNVs, as well as their genome-wide distribution and potential functions, are poorly understood in wheat. Here, we identified 198 985 DSCNVs by investigating 491 genomes of common wheat and found that they account for 20% of the entire genome. Interestingly, approximately 38% of genes are linked to DSCNVs. The number of DSCNVs within each accession ranges from 47 366 to 96 342, and their total sizes vary from 421.3 to 1267.9 Mb. We found that 957 and 1304 DSCNVs have been favored by breeders in China and the United States, respectively. By conducting DSCNV-based genome-wide association studies for the principal components of plant developmental and yield-component traits, we identified 34 loci as directly or indirectly involved in controlling the formation of multiple traits. Notably, a newly discovered DSCNV covering TaFT-D1 is significantly associated with flowering time and other agronomic traits. Overall, our findings highlight the potential of DSCNVs to drive fundamental discoveries in plant science. The comprehensive DSCNV map and the DSCNV-associated genes will also facilitate future research efforts to improve wheat yield, quality, and adaptation.

Control efficacy of Bt-Cry1Ab maize (event DBN9936) against Ostrinia furnacalis (Guenée) in Sichuan Province, China

BACKGROUND

The Asian corn borer (ACB), Ostrinia furnacalis (Guenée), is a major pest restricting maize production in Asia. The Chinese government has approved the commercial planting of Bt-Cry1Ab maize (event DBN9936), but its control potential against the ACB in southern regions remains unclear. This study evaluated the sensitivity of ACB to Cry1Ab protein expressed in Bt-Cry1Ab maize and determined the control efficacy of Bt-Cry1Ab maize against the ACB in Sichuan Province, a major maize-producing region in southern China, based on pilot planting in the field, and larval feeding bioassays in the field and laboratory.

RESULT

The Cry1Ab protein contents in different tissues of Bt-Cry1Ab maize ranged from 31.20-88.27 μg g-1. The range of median lethal concentrations (LC50) and median growth inhibitory concentration (GIC50) values of Cry1Ab protein expressed in Bt-Cry1Ab maize against ACB larvae were 0.036-0.109 μg mL-1 and 0.002-0.018 μg mL-1, respectively. The first and fourth instar ACB larvae were unable survive feeding on different tissues of Bt-Cry1Ab maize plants. Field experiments conducted from 2023 to 2024 indicated that the number of ACB larvae per 100 plants, plant damage rate, leaf damage rate, male ear damage rate, female ear damage rate, and stalk damage rate in the Bt-Cry1Ab maize fields were reduced by 95.36% ± 2.17%, 83.98% ± 1.73%, 89.45% ± 1.50%, 100.00% ± 0.00%, 69.79% ± 4.88% and 100.00% ± 0.00%, respectively, compared to conventional maize fields.

CONCLUSION

The ACB population in Sichuan Province, China is sensitive to Cry1Ab expressed in Bt-Cry1Ab maize (event DBN993). Planting Bt-Cry1Ab maize efficiently reduces the population of ACB larvae and the percentage of damaged maize plants, and has great application potential in the integrated pest management of the ACB in Sichuan Province, China. © 2024 Society of Chemical Industry.

Shifting agricultural land use and its unintended water consumption in the North China Plain

Agricultural land use (ALU) critically influences food production and water resource allocation. This study examines the dynamics of ALU in the North China Plain (NCP), a region characterized by intensive agriculture and severe groundwater over-exploitation, focusing on the multidimensional drivers and their implications for water resource management. By employing an elaborate classification scheme based on satellite imagery and extensive first-hand field data, we identified significant shifts in crop patterns. From 2013 to 2017, there was a notable transition from double crops (primarily wheat-maize) to single crops (primarily maize), covering 4600 km2 and accounting for 42% of single crops in 2013. From 2017 to 2022, there was a shift from single crops to economic forests, encompassing 3600 km2 and 22% of economic forests in 2017, including orchards, timber trees, and shelter forest belts. These shifts resulted in an 11% decrease in grain acreage (6800 km2) but an 11% increase in crop water consumption (6.3 km3) during 2013-2022. Notably, water consumption by economic forests increased by 126% (9.4 km3) during this period. This study highlights the critical need to balance competing demands for food and water security, providing valuable insights applicable to other agriculturally intensive regions worldwide.

The dataset of main grain land changes in China over 1985-2020

Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985-2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 .

Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China

Nitrogen dioxide (NO2) has decreased by ∼33% across over 1200 monitoring sites in China during 2015-2023, following a series of clean air policies. However, most of these sites are located in or near cities, leading to uncertainties in NO2 trends beyond urban regions due to limited observations. Here, we used satellite measurements to examine the differences in NO2 trends between urban and rural China. In urban areas, NO2 columns decreased by 4.0% per annum (a-1) during summer 2011-2023, consistent with bottom-up anthropogenic emission inventory and in situ measurements. In contrast, rural NO2 columns showed a slower than expected reduction (-2.6 to -0.0% a-1) during the same period. Model simulations with updates in the soil reactive oxidized nitrogen (Nr) scheme indicated that increasing soil Nr emissions can be an important factor contributing to the observed slow NO2 decrease in rural areas. This unregulated source increased summertime pollutant levels, partially offsetting the national efforts to mitigate NO2, ozone (O3), and particulate nitrate (NO3-) levels by 20.9%, 15.4%, and 4.7%, respectively, from 2011 to 2020. In the agriculture-intensive North China Plain, the increase in soil Nr emissions offset 46.6% of the NO2 reductions achieved by clean air policies. Our results highlight the increasing significance of soil emissions and the need to control them in future air-quality policies.

Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.

Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions

Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky-Golay (S-G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S-G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Assessment of bioenergy plant locations using a GIS-MCDA approach based on spatio-temporal stability maps of agricultural and livestock byproducts: A case study

Addressing the global challenge of energy sustainability and global directives on farming emissions, the United Nations, the European Union, and China have led with strict targets for clean energy, renewable share growth, and carbon neutrality, highlighting a commitment to collective sustainability. This work is situated within the ambit of the Sustainable Development Goals (SDGs), advocating for a transition towards renewable energy sources. With substantial and accessible bioenergy resources, notably in Hubei Province, China, biogas technology has emerged as an emission-cutting solution. This research, focused on the Jianghan Plain, employs an integrated approach combining spatial analyses with machine learning tools to evaluate crop yield stability over two decades, with the aim of maximising the biogas yield from agricultural byproducts, i.e., crop straw and livestock manure. Using Multi-Criteria Decision Analysis (MCDA), which is informed by grey-based DEMATEL, 9 constraints and 13 environmental, social, and economic criteria were assessed to identify optimal sites for biogas facilities. The findings underscore the significant bioenergy potential of agricultural byproducts from the plain of 6.3 × 1012 kJ/year at an 11.4 kJ/m2 density. Stability analyses revealed consistent biomass availability, with rice in Gongan and Shayang and wheat in Jiangling being the primary contributors. Through the MCDA, 45-66 optimal biogas plants were identified across 4 critical counties (Zhongxiang, Shangyang, Jingshan, and Yichen), balancing the energy supply and demand under various stable scenarios. Furthermore, this study demonstrated the criticality of moderate biomass stability for stakeholder consensus and identified areas of high stability essential for energy demand fulfilment. Theoretically, this study offers a practical model for bioenergy resource exploitation that aligns with global sustainability and carbon neutrality goals to address the urgent need for renewable energy solutions amidst the global energy crisis. Practically, this study sets a precedent for policy and planning in environmental, agricultural, and renewable sectors, signifying a step forwards in achieving environmental sustainability and an energy-efficient future.

National-scale 10-m maps of cropland use intensity in China during 2018-2023

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

CROPGRIDS: a global geo-referenced dataset of 173 crops

CROPGRIDS is a comprehensive global geo-referenced dataset providing area information for 173 crops for the year 2020, at a resolution of 0.05° (about 5.6 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset (hereafter MRF), the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS builds on information originally provided in MRF and expands it using 27 selected published gridded datasets, subnational data of 52 countries obtained from National Statistical Offices, and the 2020 national-level statistics from FAOSTAT, providing more recent harvested and crop (physical) areas for 173 crops at regional, national, and global levels. The CROPGRIDS data advance the current state of knowledge on the spatial distribution of crops, providing useful inputs for modelling studies and sustainability analyses relevant to national and international processes.

High-resolution atmospheric mercury emission from open biomass burning in China: Integration of localized emission factors and multi-source finer resolution remote sensing data

Mercury (Hg) emissions from open biomass burning represent one of the largest Hg inputs to the atmosphere, with considerable effects on the atmospheric Hg budget. However, there is currently large uncertainty in the inventory of Hg emissions from open biomass burning in China due to limitations on the coarse resolution of burned area products, rough biomass data, and the unavailability of suitable emission factors (EFs). In this study, we developed high tempo-spatial resolution (30 m) and long time-series (2000-2019) atmospheric Hg emission inventories from open biomass burning using the Global Annual Burned Area Map (GABAM) product, high-resolution biomass map, Landsat-based tree cover datasets as well as local EFs in China. The results showed that the average annual Hg emission from open biomass burning in China amounted to 172.6 kg during 2000-2019, with a range of 63-398.5 kg. The largest Hg emissions were found in cropland (72%), followed by forest (25.9%), and grassland (2.1%). On a regional level, Northeast China (NE) and Southwest China (SW) were the two main contributors, together accounting for more than 60% of total Hg emissions. The temporal distribution of Hg emissions showed that the peaks occurred in 2003 and 2014. This is a comprehensive estimation of Hg emissions from open biomass burning in China by integrating various high-resolution remotely sensed data and nationwide localized EFs, which has important implications for understanding the role of open biomass burning in China in regional and global atmospheric Hg budget.

Large Contribution of Nitrous Acid to Soil-Emitted Reactive Oxidized Nitrogen and Its Effect on Air Quality

Soil emissions have long been recognized as an important source of nitric oxide (NO), which regulates atmospheric oxidative capacity and the production of air pollutants. Recent research has also indicated that nitrous acid (HONO) can be emitted in significant quantities from soil microbial activities. However, only a few studies have quantified emissions of HONO along with NO from a wide range of soil types. In this study, we measured emissions of HONO and NO from soil samples collected from 48 sites across China and found much higher emissions of HONO than of NO, especially for samples from northern China. We performed a meta-analysis of 52 field studies in China, which revealed that long-term fertilization increased the abundance of nitrite-producing genes much more than the abundance of NO-producing genes. This promotion effect was greater in northern China than in southern China. In simulations using a chemistry transport model with laboratory-derived parametrization, we found that HONO emissions had a greater effect than NO emissions on air quality. Moreover, we determined that with projected continuous reductions in anthropogenic emissions, the contribution from soils to maximum 1 h concentrations of hydroxyl radicals and ozone and daily average concentrations of particulate nitrate in the Northeast Plain will increase to 17%, 4.6%, and 14%, respectively. Our findings highlight the need to consider HONO in the assessment of the loss of reactive oxidized nitrogen from soils to the atmosphere and its effect on air quality.