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Enhancing intercropping efficiency: A new model for precise quantification of shading impact in maize-soybean systems

Enhancing intercropping efficiency: A UAV-based model for precise quantification of shading impact in maize-soybean systems
The workflow for high-throughput estimation of canopy height differences between soybean strip and the adjacent maize strips, including UAV image acquisition and processing (1), the establishment of canopy height model (CHM) (2), and the calculation of Hms (3). UAV image acquisition and processing were composed of UAV campaigns (A) and strip segmentations on UAV-derived digital ortho-mosaic (B) and digital surface model (DSM; C). The establishment of a CHM included the calculation of the difference between DSM and the reference ground (D) and the removal of soil and vegetative pixels of the other adjacent crop (E) from the CHM. Lastly, the strips of maize (G) and soybean (H) were separated by the threshold (F), and the canopy heights were then estimated from the corresponding CHMs, followed by the calculation of Hms. Credit: Plant Phenomics

Intercropping, the simultaneous cultivation of multiple crops, is beneficial for maximizing resource use but poses challenges due to the shading of lower crops by higher ones. This shading, quantified as Cumulative Shading Capacity (CSC), significantly affects light interception and crop efficiency but remains poorly understood. Previous research mainly has focused on light distribution and crop responses without directly quantifying CSC and its variability across rows. Despite attempts using geometrical and statistical models, accurate, direct CSC quantification and understanding inter-row heterogeneity remain elusive.

A research team has published a research article in Plant Phenomics on this topic, titled "Quantification of the cumulative shading capacity in a maize-soybean intercropping system using ."

In this study, the researchers utilized unmanned aerial vehicles (UAVs) to measure canopy height differences and solar position, leading to the development of a Shading Capacity Model (SCM) for quantifying the shading impact on soybeans in maize-soybean intercropping systems. The SCM demonstrated that the southernmost row of soybean consistently experienced the highest shading proportion, with significant variations across strip configurations and plant densities.

The maximum cumulative shading capacity (CSC) observed was 123.77 MJ m-2. A quantitative relationship was established between CSC and soybean canopy height increment, indicating that as the canopy height increased, so did the CSC. The model also assessed the effects of canopy height difference, latitude, and planting direction on shading, finding that greater height differences and latitudes resulted in increased shading, while a planting direction between 90° to 120° minimized it.

The study found that the shading distance of maize on adjacent soybean rows changed over growth stages, influenced by canopy height and solar trajectory. Detailed analysis revealed that the first row of soybean typically had the largest shading proportion, with the amount of shading differing significantly among different intercropping treatments and configurations. Overall, treatments with smaller distances between maize and soybean (Dms) experienced greater shading.

Furthermore, a positive correlation was identified between the canopy height increment of soybeans and CSC, implying that taller soybeans encountered increased shading. Finally, the study examined how different factors influenced the overall shading proportion of the strip. It was noted that the height difference, latitude, and planting direction significantly affected the shading proportion, with certain directions and configurations minimizing shading effects.

Overall, this study is valuable for optimizing intercropping patterns by adjusting factors like planting configurations and directions, indicating potential for future applications in enhancing crop growth, light competition, and microclimate management on a larger scale through integration with crop models or remote sensing data.

More information: Min Li et al, Quantification of the Cumulative Shading Capacity in a Maize–Soybean Intercropping System Using an Unmanned Aerial Vehicle, Plant Phenomics (2023). DOI: 10.34133/plantphenomics.0095

Provided by Plant Phenomics

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