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Agricultural Climatic Factors and Their Thresholds for Winter Wheat Cultivation in Northern China CHEN Shi, HUANG YinLan, JIN YunXiang, XU ChengLin, ZOU JinQiu Scientia Agricultura Sinica    2024, 57 (16): 3142-3153.   DOI: 10.3864/j.issn.0578-1752.2024.16.004 Abstract （217）   HTML （17）    PDF （4427KB）（581）       Knowledge map    Save 【Background】To determine the safe planting limit of winter wheat based on agricultural climate indicators is crucial for the scientific and rational utilization of resources, avoiding freezing disasters, and ensuring stable and high yields of winter wheat. However, in the north of China, which is located in the sensitive area of winter wheat planting, the fluctuation of safe winter wheat planting has been intensified due to the increase of extreme weather events caused by global climate change. It is urgent to clarify the agroclimatic factors affecting the safe planting of winter wheat on a large regional scale and to determine their threshold ranges. 【Objective】The research on the agricultural climatic factors and their thresholds for the safe planting of winter wheat was conducted to provide a scientific basis for the sustainable production and planning of winter wheat in response to climate change. 【Method】The northern China was selected as the research area, which was highly sensitive to the safe planting of winter wheat. Based on the spatial distribution of winter wheat with medium and high spatial resolution and ground meteorological observation data, this research utilized methods such as kernel density estimation, geographic detector to reveal the spatial pattern characteristics of the actual northern limit of winter wheat planting, to quantitatively analyze the influence of agricultural climate factors on the formation of the actual northern limit of winter wheat planting, and to explore the threshold of key climate factors. 【Result】(1) The actual northern limit of winter wheat planting, with a total length of about 2 200 km, fluctuated from southwest to northeast. However, agricultural climate factors exhibited more significant fluctuations along the line of Pingning-Xunyi-Tongchuan-Baishui- Heyang-Hancheng-Jishan. (2) The negative accumulative temperature during winter, average temperature of the coldest month, extreme minimum temperature of the year, and accumulative temperature before winter were crucial factors (q >0.45) in shaping the actual northern limit of winter wheat planting. Agricultural precipitation factors had a minor effect (q <0.19) on winter wheat planting, but interacted strongly with temperature factors (q >0.57). (3) Specific meteorological parameters for the northern limit of winter wheat safe planting in northern China were established: negative accumulated temperature in overwintering period≥-620 ℃·d, coldest monthly mean temperature≥-8 ℃, annual extreme minimum temperature≥-22 ℃, and accumulated temperature before overwintering≥529 ℃·d. (4) The potential northern limit for winter wheat planting has moved about 107 km northward compared to the actual limit, with approximately 23.39×103 km2 of expansion area. 【Conclusion】This study identified the key agricultural climate indicators and thresholds influencing safe winter wheat planting in northern China, which provided a basis for determining potential safe planting areas for winter wheat. The research results could provide the theoretical reference and technical support for how winter wheat planting could adapt to climate change and adjust agricultural planting layout reasonably. Table and Figures | Reference | Related Articles | Metrics

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Nitrogen Nutrition Estimation of Maize Based on UAV Spectrum and Texture Information YUN BinYuan, XIE TieNa, LI Hong, YUE Xiang, LÜ MingYue, WANG JiaQi, JIA Biao Scientia Agricultura Sinica    2024, 57 (16): 3154-3170.   DOI: 10.3864/j.issn.0578-1752.2024.16.005 Abstract （226）   HTML （20）    PDF （6561KB）（1120）       Knowledge map    Save 【Objective】Crop nitrogen nutrition status is a key indicator to characterize the green degree and health status of maize canopy. In order to compare the accuracy of single spectral index model and texture information fusion model in maize nitrogen nutrition estimation model, this investigated the accuracy and reliability of maize nitrogen nutrition estimation model based on UAV multispectral information and texture information fusion. 【Method】 Matrice-300 RTK multi-rotor aircraft equipped with MS600 Pro multi-spectral sensor was used to obtain multi-spectral images of maize tasseling-silking stages under six nitrogen levels in two years. By extracting vegetation index and texture features, the correlation between vegetation index, single texture feature, combined texture index and fusion information of vegetation index and texture index, was comprehensively analyzed. The vegetation index, normalized difference texture index (NDTI) and their combined parameters with the largest amount of information were selected. Four nitrogen nutrition parameters of maize leaf nitrogen content (LNC), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) were compared and estimated by multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM), and grey wolf optimized convolutional neural network ( GWO-CNN ). 【Result】 (1) There were differences in the original spectral reflectance of maize under different nitrogen treatments, and the differences in the red band R (660 nm), blue band B (450 nm) and near-infrared band NIR (840 nm) were significant. (2) The vegetation indices (EVI, GARI, REOSAVI, SIPI, and MCARI), single texture features (var450, var660, mean840, dis720, and hom840) and combined texture index NDTI extracted from UAV multispectral images could be used for LNC, PNC, LNA and PNA estimation of maize in VT-R1 stage. The GWO-CNN model based on vegetation index had better estimation effect on LNC, PNC, LNA and PNA than single texture feature and texture index model, and its R2 were 0.831, 0.761, 0.826 and 0.770, respectively. (3) The accuracy of GWO-CNN model with vegetation index and texture index for LNC, PNC, LNA and PNA estimation was significantly higher than that of vegetation index and texture index, and its R2 was 0.921, 0.901, 0.917 and 0.892, respectively, which was 9.77%, 15.54%, 9.92% and 13.68% higher than that of single spectral information optimal estimation model. 【Conclusion】 Fusion of multi-spectral vegetation index and texture index could effectively improve the estimation accuracy of maize nitrogen nutrition, and better evaluate the distribution of maize nitrogen distribution, which provided new ideas for precise maize nitrogen fertilizer management based on UAV platform at field scale. Table and Figures | Reference | Related Articles | Metrics

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