1) Legumes used as cover crops before planting sugar beets can impact nitrous oxide (N2O) emissions. Data from experimental fields in France from 2009-2015 was analyzed.
2) Results showed that all cover crops reduced nitrate leaching compared to bare soil, and also reduced N2O emissions. Mixed cover crops of crucifers and legumes were the most effective at reducing nitrate leaching and nitrogen fertilizer usage, leading to the largest N2O emission reductions.
3) When the nitrogen from cover crop residues was accounted for in the calculations, the overall emission reduction was less positive, with legume-only and mixed cover crops being penalized the most. Further modeling work
This study evaluated using multispectral drone imagery to phenotype sugar beet crops. High-resolution drone images allowed separating soil and vegetation pixels, improving estimates of green fraction, green area index, and leaf chlorophyll content compared to standard approaches. The best method was calculating vegetation indices like VARI on all pixels for structure, and mNDblue on dark pixels for chlorophyll. This fine-scale remote sensing has potential to better characterize genotypes and monitor crop growth over time.
1) Legumes used as cover crops before planting sugar beets can impact nitrous oxide (N2O) emissions. Data from experimental fields in France from 2009-2015 was analyzed.
2) Results showed that all cover crops reduced nitrate leaching compared to bare soil, and also reduced N2O emissions. Mixed cover crops of crucifers and legumes were the most effective at reducing nitrate leaching and nitrogen fertilizer usage, leading to the largest N2O emission reductions.
3) When the nitrogen from cover crop residues was accounted for in the calculations, the overall emission reduction was less positive, with legume-only and mixed cover crops being penalized the most. Further modeling work
This study evaluated using multispectral drone imagery to phenotype sugar beet crops. High-resolution drone images allowed separating soil and vegetation pixels, improving estimates of green fraction, green area index, and leaf chlorophyll content compared to standard approaches. The best method was calculating vegetation indices like VARI on all pixels for structure, and mNDblue on dark pixels for chlorophyll. This fine-scale remote sensing has potential to better characterize genotypes and monitor crop growth over time.