This study aimed to investigate the spatiotemporal variations in the grain-to-straw ratios of wheat and maize. A systematic evaluation was also made on the carbon sequestration and emission reduction potential of the straw utilization. Meta-analysis was conducted to search for the keywords, such as "straw-to-grain ratio," "harvest index," "wheat," "maize," and "biomass" in the China National Knowledge Infrastructure, and then screened the straw-to-grain ratio and crop yield data. The straw-to-grain ratio and straw nutrient analysis were then implemented on the temporal variations in the straw-to-grain ratio and its relationship with the crop yield. A total of 285 wheat and 173 maize samples were collected from 14 counties in Henan Province, China. Straw production and utilization data were integrated from the Crop Straw Resource Accounting Subsystem at the county level. The nutrient content of the straw was calculated to evaluate the carbon sequestration and emission reduction potential of straw utilization. The fertilizer substitution of the straw incorporation was also assessed after calculation. The results show that the grain-to-straw ratio of the wheat and maize decreased by 0.14 and 0.06, respectively, every five years. The grain-to-straw ratio decreased by 0.36 for wheat and 0.09 for maize, particularly for every 1 000 kg increase in yield per hectare. The eastern region shared the highest grain-to-straw ratio for wheat (1.14), while the central and western regions had the lowest (1.00). In maize, the southern region exhibited the highest grain-to-straw ratio (0.9), while the eastern and northern regions had the lowest (0.77). The average annual straw production of wheat and maize was 5 168.9×104 t and 2 788.4 × 104 t, respectively, from 2021 to 2023. In terms of the straw utilization, over 98% of the straw was used for fertilization, feed, and fuel. Specifically, 87.7% of the wheat straw was used for fertilization, 8.7% for feed, and 2.3% for fuel, while 80.9% of maize straw was used for fertilization, 14.7% for feed, and 3.6% for fuel. The straw return was contributed approximately 9 579.9×104 t of wheat straw and 5.751.7×104 t of maize straw to croplands over the past three years, thus returning 102.8×104 t of nitrogen, 36.14×104 t of phosphorus, and 367.87×104 t of potassium to the soil, with an average annual carbon sequestration of 259.44×104 t. The fertilizer, feed, and fuel utilization of the straw was equivalent to carbon emissions reduced by 252.99×104 t annually. The grain-to-straw ratios of the wheat and maize continuously decreased with the time progression and yield improvement. The utilization of the straw can be expected to improve the soil fertility and reduce carbon emissions. Therefore, the straw utilization was integrated for green agriculture in the goals of carbon peaking and carbon neutrality.
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Traditional two-dimensional (2D) analyses of maize (Zea mays L.) plant shape plasticity and canopy transmission under varying planting densities have limitations in capturing spatial heterogeneity. This study used a three-dimensional (3D) phenotyping platform to investigate architectural plasticity across different maize varieties and planting densities. Seven novel 3D architectural parameters were developed, and 3D canopy models were constructed for light distribution simulation. At the vegetative stage 9 (V9) stage, medium planting density (67,500 plants ha–1, MD) increased plant side width and convex hull volume by 7.2 and 11.4%, respectively, compared to low planting density (37,500 plants ha–1, LD). High planting density (97,500 plants ha–1, HD) increased the width and volume by 4.2 and 17.8%, respectively, compared with MD. Similar changes were maintained at the V13 stage. At the silking stage, the number of voxel volume plant (NVP) and projected area (PJA) decreased by 6.2 and 11.9%, respectively, under MD compared with LD, and by 4.9 and 3.6%, respectively, under HD compared with MD. Across all densities, PJA and NVP in both MC812 and JNK728 were consistently lower than in ZD958. A bottom light transmittance estimation model combining point cloud parameters with support vector regression achieved reliable predictions (R2=0.76, RMSE=2.89%). The 3D canopy model effectively simulated population light distribution (R2=0.83, RMSE=8.53%). NVP and PJA were identified as critical parameters affecting bottom canopy transmittance, suggesting their potential as 3D selection indices for maize density tolerance breeding. These findings provide insights into stage-specific architectural plasticity and light interception, supporting molecular design breeding of density-tolerant maize.
The aim of the study was to clarify the main direction of further improving wheat grain yield in the different wheat regions of Henan province by analyzing the temporal and spatial differences of wheat yield and yield components.
The spatial distribution maps of wheat grain yield and yield components in four wheat regions of Henan province from 2017 to 2020 were drawn based on the field monitoring data of fixed wheat monitoring stations in Henan province, and the optimal model was selected by geographic information system (GIS), and then the differences and relationships among different wheat regions were analyzed.
The wheat yield and yield components were different between different wheat regions. Among them, the yield and the spike number in North Henan and Central Henan were significantly higher than those in South Henan and West Henan, and the North Henan's were the most, while the West Henan's were the least. However, the kernels per ear showed that the production regions in Central Henan, South Henan and North Henan were significantly more than West Henan's, and the most in Central Henan, while the least in West Henan. The 1000-grains weight in North Henan was the most, while South Henan was the lowest. The wheat yield, spike number, kernels per ear, and 1000-grains weight in Central Henan and South Henan (Luohe, Zhoukou, Zhumadian, etc.) were often more than that of other places in Henan, and this performance were stable between years. Correlation analysis showed that the relationship between the three elements of yield and yield in different wheat regions was inconsistent. Specifically speaking, the 1000-grains weight, the kernels per ear and the spike number in the North Henan and Central Henan regions had the largest correlation with the yield. However, the relationship with yield in East Henan and South Henan were appeared as: the spike number was the largest, the 1000-grains weight was the second, and the kernels per ear was the smallest. Path analysis was carried out on the three elements of yield and yield in those four wheat regions, which further showed that there were differences in the contribution of the yield components to yield. More precisely, the spike number and kernels per ear contributed the most to the yield in North Henan, with a direct path coefficient of 0.67. The contribution of yield components to yield in Central Henan and South Henan regions was spike number> kernels per ear> 1000-grain weight; while in West Henan, the greatest was the spike number, followed by 1000-grain weight, and the kernels per ear was the least; the direct path coefficients were 0.69, 0.45 and 0.39, respectively. Meanwhile, the indirect diameter coefficient showed that enhancing the yield increase effect of the 1000-grain weight was better than that of the kernels per ear in North Henan, Central Henan, and South Henan regions, but the West Henan was better by enhancing the kernels per ear.
There were large differences in wheat yield and yield components in Henan different wheat regions and between years. At the same time, the three components of wheat yield in different wheat regions had different contributions to yield. Therefore, in term of further tapping the potential of wheat production for Henan province, it should be accurately classified by regions and years. As far as the conditions of this experiment concerned, based on stabilizing the spike number in the Henan province wheat regions, the production regions of North Henan, Central Henan, and South Henan should focus on further tapping the potential of 1000-grain weight, while the West Henan improving the yield increase effect of the kernels per ear were better than that of the 1000-grain weight.
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