The increase in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] production in rainfed cropping systems of the northern Great Plains (NGP) has altered the delivery of ecosystem services from agricultural land. To date, there is limited understanding about how these crops affect the carbon (C) balance of cropping systems in the region when included in a short rotation window. This study sought to quantify C balance of a spring wheat (Triticum aestivum L.)–corn—soybean rotation under no‐tillage management using eddy covariance (EC) techniques. Paired field sites with the same soil type near Mandan, ND, were used for the study. Annual net ecosystem production (NEP) by crop was –34, 120, and 7 g C m^–2 y^–1 for spring wheat, corn, and soybean, respectively. Carbon removed in grain over the rotation was less than a third of C lost by ecosystem respiration (ER) (210 vs. 674 g C m^–2 y^–1). After accounting for grain C removal, net ecosystem carbon balance (NECB) was –164, –253, and –121 g m^–2 y^–1 for spring wheat, corn, and soybean, respectively, with a mean NECB of –179 ± 39 g C m^–2 y^–1 (P = .043; t test for difference from zero), implying the rotation was a net C source. Time associated with active crop growth each year was 27% for spring wheat, 41% for corn, and 28% for soybean. Management strategies that lengthen the period of biomass growth may mitigate C loss from spring wheat–corn–soybean rotations in the NGP.

A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 $\mathrm{h}$–14:00 $\mathrm{h}$) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network.

Cropland agriculture in the northern Great Plains is challenged by variable weather, agricultural intensification, and competing use for energy development. Innovative cropland practices that address these challenges are needed to ensure regional agriculture can sustainably meet future food, fuel, and fiber demand. In response to this need, the Northern Plains Long‐Term Agroecosystem Research network site established a cropland experiment in 2019 that contrasts prevailing and alternative practices at plot and field scales over a proposed 30‐year time frame. The experimental site is located on the Area IV Soil Conservation Districts Cooperative Research Farm near Mandan, ND. Cropping practices for the first 6 years of the experiment were developed with input from stakeholders and include a 3‐year crop rotation of spring wheat (Triticum aestivum L.), corn (Zea mays L.), and soybean (Glycine max L.) with cover crops (alternative practice) and without (prevailing practice). The prevailing practice also involves the removal of crop residue, while a second alternative practice of perennial forages is included in the plot‐scale experiment. Biophysical measurements are made at both spatial scales at frequencies aligned with approved methods for each agronomic and environmental metric. Findings from the first 6 years of the experiment will help identify tradeoffs associated with cover crop use and residue removal in dryland cropping systems. In the future, the experiment will adopt a knowledge co‐production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research, and interpret results.