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Core Ideas Compared corn–soybean rotations with cover crops vs fallows under no‐till or till. Corn–soybean rotations with cereal rye after corn decreased soil NO3–N by 42%. Soil attributes and crop yields were generally unaffected by cover crops use. Tillage increased soil organic matter and exchangeable K compared to no‐till. Tillage reduced soybean yields by 245 kg/ha compared to no‐till. Cover crops (CCs) have been heralded for their potential to improve soil properties, retain nutrients in the field, and subsequent crop yields, yet support for these claims within Illinois remains limited. Cover crops were used in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotations. We assessed five sets of CCs vs. fallow controls under no‐till (NT) and chisel till (Till) on soil attributes and crop yields, encompassing one complete rotation cycle. The experimental layout was a split split‐block where whole plot treatments (P, rotation phase; and Y, year) had a Latin square design and subplot treatments of tillage (NT vs. Till) were split into sub‐subplot treatments of CC rotations. We measured soil properties, crop yields, CC stand counts in late fall, and spring biomass samples, each year. Tillage increased the level of soil organic matter (SOM) and exchangeable potassium (K) within our systems yet significantly decreased the yield of soybean by 245 kg/ha. Compared to winter fallow, soil attributes under corn–soybean rotations that included CCs did not show any statistically significant change after one cycle of production except increased N scavenging with cereal rye growing after corn harvest. Inclusion of CCs in the corn–soybean rotation did not affect cash crop yields in either till or NT systems. Our results show that cereal rye is the CC with the best potential as an N scavenger in the corn–soybean rotation, but claims of crop yield increases in the short term are not supported.

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here. * indicates joint first author. For more information on our database and other related efforts in Agriculture-Vision, please visit our CVPR 2020 workshop and challenge website https://www.agriculture-vision.com.

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