Peng Yang’s research while affiliated with Chinese Academy of Agricultural Sciences and other places

Northeast China (NEC) as one of the primary breadbaskets of China plays an essential role in achieving sustainable agriculture to provide sufficient and nutritious food while minimizing resource consumption and environmental costs. Growing evidence indicates crop switching is a promising solution for achieving sustainable agriculture. Comprehensively assessing synergies and tradeoffs among competing objectives for stakeholders is essential for crop switching implementation but not well documented in NEC. We examine tradeoffs and synergies among multi-objectives—nutritional yields, water demand, greenhouse gas emissions (GHGs), and benefits—from policymakers’ and farmers’ perspectives for cereals in NEC using the most recent data available, and assess potential sustainability changes from implementing the policy of crop switching. We find no single cereal can achieve all objectives of sustainable agriculture in most regions of NEC for stakeholders and synergies and tradeoffs have obviously spatial heterogeneity. Overall, rice has the best performance on energy and protein yield but the worst on iron yield, water requirement, and GHGs. Coarse cereals (sorghum and millet) have better desirable attributes on iron yield 223% and 66% more, blue water requirement 91% and 90% less, and GHGs 84% less than rice, but not for energy and protein yield because of lower yields. From the farmers’ perspective, rice can produce more revenue than dryland cereals by 32%–58% due to higher price and yield. Nevertheless, the sustainability of cereal production in NEC will be improved from crop switching with a 33% increment in iron production, a 24% and 3% decrease in irrigation water demand and GHGs, and a 4% increment in farmers’ revenue on existing cultivation area without compromises in rice production. Our study indicates that comprehensively assessing the synergies and tradeoffs among multiple objectives and stakeholders will provide more opportunities to align policymakers with practitioners to make crop switching feasible and achieve sustainable agriculture.

Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference data and the limited transferability capabilities across large spatial domains. This study aimed to fill these gaps by proposing a robust Complex Cropping Pattern Mapping framework (CCPM) capable of national-scale automatic applications using the Sentinel-1 SAR and Sentinel-2 MSI time series datasets. The CCPM framework addresses these challenges by integrating knowledge-based approaches & data-driven algorithms (Dual-driven model) and Phenological Normalization. The CCPM framework was implemented over conterminous China with complex cropping systems dominated by smallholder farms, and the first national-scale 10-m Cropping pattern map with descriptions of cropping intensity and 10 crops in China (ChinaCP-T10) in 2020 was produced. The efficiency of the CCPM framework was validated when evaluated by 18,706 ground-truth reference datasets, with an overall accuracy of 91.47 %. Comparisons with existing crop data products revealed that the ChinaCP-T10 offered more comprehensive and consistent information on diverse cropping patterns. Dominant cropping patterns diversified from single maize in northern China, winter wheat-maize in North China Plain, single oilseeds in Western China, to single rice or double rice in Southern China. The key cropping patterns changed from double-grain cropping, single grain to single cash cropping with increasing altitudes. There were 151,744 km 2 planted areas of double grain cropping patterns in China, and multiple cropping accounted for 36.1 % of grain cultivated area nationally. Over 80 % of grain production was mainly implemented at lower altitudes as the Non-Grain Production (NGP) ratio enhanced from 32 % within elevations below 200 m to over 70 % among elevations above 700 m. Consistent datasets on complex cropping patterns are essential, given the significant roles of diversification and crop rotations in sustainable agriculture and the frequently observed inconsistencies in existing crop data products based on thematic mapping.

There are severe confficts between grain production and ecological beneffts, and how to explore the critical conffgurations of agricultural landscapes and natural habitats to clarify sustainable scenarios remains unclear. Thus, this study explored a transferable approach to generating the production possibility frontiers of trade-offs between grain production and ecological beneffts (biodiversity, carbon sink, and water consumption) under unconstrained, ecological constraint, and agricultural and ecological constraint scenarios, and identiffed the threshold and safety area for landscape optimization in the Beijing–Tianjin–Hebei (BTH) region of China. When reaching the Pareto optimality of trade-off frontiers, the grain yield and biodiversity increased by 10%–17%, the grain yield and carbon sink increased by 15%–48%, and the grain yield and water consumption improved by 4%– 25%. The grain production and ecological beneffts were outside the safety area in the BTH region, and the landscape optimization strategy was different for each trade-off. Both the food and biodiversity security can be further improved through increasing by 2.7% of cropland in the BTH region. The land use strategy of converting 6.8% of the cropland of the BTH region into forest land can promote carbon sink security. Although the land use strategy of converting 2.3% of the cropland of the BTH region into grassland can promote water security, more effort should be focused on technological innovation. This study highlights that landscape optimization will promote landscape multifunctionality and provides quantitative landscape optimization thresholds and safety boundaries for improving grain production and ecological beneffts.

[Objective] By analyzing the existing literature, this study aimed to clarify the research hotspots and application progress of digital twin technology in the field of agriculture, and provide future directions for the field of agricultural digital twin in China. [Methods] By using bibliometric and keyword clustering analysis, we summarized the research hotspots, technical frameworks, application scenarios, and service categories of agricultural digital twins. [Results] (1) Agricultural digital twin exhibits promising prospects, and the overall publications showed an upward trend, with a significant increase after the year 2020; (2) Combining with different application scenarios in agriculture production, agricultural digital twin system is primarily built using technologies such as the Internet of Things, artificial intelligence, and machine learning, enabling the visualization and digitization of intelligent agricultural system management; (3) The main application scenarios of digital twin technology in the agricultural field are agricultural planting scenarios such as agricultural products, agricultural machinery, irrigation, and agricultural sideline industries such as food supply chain. And the service categories it provides focus on real-time monitoring and effect detection of agricultural scenarios. [Conclusion] Applying digital twin technology to the agricultural field is of great significance for improving the quality of agricultural products, integrating and optimizing agricultural resources, and further promoting the development of smart agriculture. In the future, greater emphasis should be placed on developing and enhancing the theoretical foundation and system framework of agricultural digital twins. Additionally, a more in-depth exploration of multi-technology integration and multi-model fusion is essential to effectively advance the development of agricultural digitization.

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R² between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R²) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

Northeast China, a significant production base for paddy rice, has received lots of attention in crop mapping. However, understanding the spatiotemporal dynamics of paddy rice expansion in this region remains limited, making it difficult to track the changes in paddy rice planting over time. For the first time, this study utilized multi-sensor Landsat data and a deep learning model, the full resolution network (FR-Net), to explore the annual mapping of paddy rice for Northeast China from 1985 to 2023 (available at https://doi.org/10.6084/m9.figshare.27604839.v1, Zhang et al., 2024). First, a cross-sensor paddy training dataset comprising 155 Landsat images was created to map the paddy rice. Then, we developed the annual result enhancement (ARE) method, which considers the differences in category probability of FR-Net at different stages to diminish the impact of the limited training sample in large-scale and across-sensors paddy rice mapping. The accuracy of the paddy rice dataset was evaluated using 107954 ground truth samples. In comparison to traditional rice mapping methods, the results obtained using the ARE method showed a 6 % increase in the F1 score. The overall mapping result obtained from the FR-Net model and ARE methods achieved high user accuracy (UA), producer accuracy (PA), F1 score, and Matthews correlation coefficient (MCC) values of 0.92, 0.95, 0.93, and 0.81, respectively. The study revealed that the area used for paddy rice cultivation in Northeast China increased from 1.11×104 km2 to 6.45×104 km2. Between 1985 and 2023, there was an overall expansion of 5.34×104 km2 in the paddy rice cultivation area, with the highest growth (4.33×104 km2) occurring in Heilongjiang province. This study shows that long-history crop mapping could be achieved with deep learning, and the result of paddy rice will be beneficial for making timely adjustments to cultivation patterns and ensuring food security.

Accurately predicting arsenic (As) concentration in farmland soil on a large scale is essential for effectively preventing and managing soil pollution in agricultural areas, thereby safeguarding food security. Multispectral imaging presents a cost-effective and efficient method for monitoring As concentration across extensive farmland regions. Nevertheless, the underlying process and mechanisms determining the relationship between As concentration in farmland soil and spectral data remain uncertain. The primary aim of this study was to evaluate whether employing a hierarchical strategy (based on soil organic matter (SOM) and pH) results in more accurate prediction of As concentration in farmland soil than those employing nonhierarchical (global) models. Our results show that with respect to global models, the best prediction of As concentration was achieved using the convolutional neural network (CNN) model (validated ratio of the model performance to the interquartile distance (RPIQ) = 2.50), followed by the Cubist model (validated RPIQ = 2.19) and the extreme learning machine (ELM) model (validated RPIQ = 2.10). After SOM-based hierarchization, the Cubist model exhibited the highest prediction accuracy (validated coefficient of determination (R²) = 0.73), representing a 0.02 improvement in the R² compared with the that of global CNN model. The clay minerals ratio (CMR) was identified as the most important variable for predicting As concentration. Notably, the identification of high As concentration in the central old town areas underscores the importance of early soil contamination risk warnings on arable lands. These findings indicate that SOM-hierarchical machine learning models could serve as an effective approach to addressing the influence of soil environmental complications on spectral prediction of As concentration in farmland soil. By implementing this proposed method, soil environmental monitoring efforts can be significantly improved.