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Abstract The Kellogg Biological Station Long‐term Agroecosystem Research site (KBS LTAR) joined the national LTAR network in 2015 to represent a northeast portion of the North Central Region, extending across 76,000 km²of southern Michigan and northern Indiana. Regional cropping systems are dominated by corn (Zea mays)–soybean (Glycine max) rotations managed with conventional tillage, industry‐average rates of fertilizer and pesticide inputs uniformly applied, few cover crops, and little animal integration. In 2020, KBS LTAR initiated the Aspirational Cropping System Experiment as part of the LTAR Common Experiment, a co‐production model wherein stakeholders and researchers collaborate to advance transformative change in agriculture. The Aspirational (ASP) cropping system treatment, designed by a team of agronomists, farmers, scientists, and other stakeholders, is a five‐crop rotation of corn, soybean, winter wheat (Triticum aestivum), winter canola (Brassicus napus), and a diverse forage mix. All phases are managed with continuous no‐till, variable rate fertilizer inputs, and integrated pest management to provide benefits related to economic returns, water quality, greenhouse gas mitigation, soil health, biodiversity, and social well‐being. Cover crops follow corn and winter wheat, with fall‐planted crops in the rotation providing winter cover in other years. The experiment is replicated with all rotation phases at both the plot and field scales and with perennial prairie strips in consistently low‐producing areas of ASP fields. The prevailing practice (or Business as usual [BAU]) treatment mirrors regional prevailing practices as revealed by farmer surveys. Stakeholders and researchers evaluate the success of the ASP and BAU systems annually and implement management changes on a 5‐year cycle. 

Soil zymography is a new technique developed to visualize two‐dimensional distributions of enzyme activities. The method consists of incubating a membrane saturated with an enzyme‐specific fluorogenic substrate on a surface of the soil sample, followed by recording the membrane image generated by a fluorescent product (e.g. MUF: methylumbelliferone) in ultraviolet light. Despite its relative ease of use, performing zymography involves multiple user‐made decisions that might affect the accuracy of enzyme activity estimates. Therefore, unification of the zymography methodology is required for correct estimations and comparisons of various studies. We evaluated the following methodological aspects of the implementation of zymography: (a) camera settings and image processing, (b) effects of evaporation and (c) calibration procedures. Camera settings (shutter speeds or exposure time) affected the intensity of background fluorescence and signal‐to‐noise ratios (SNR). However, because their combined effects varied depending on MUF concentrations, light and camera setting need to be optimized for the expected range of MUF concentrations prior to zymography. Evaporation of MUF solution from the membrane had no effect on fluorescence. Relations between MUF concentration and intensity of fluorescence during calibrations demonstrated a saturated pattern and were strongly affected by image noise outside the optimal range (e.g. 8–14 μmMUF pixel⁻¹). We developed a new calibration approach that is based on a piecewise linear regression. The new approach accounted for specific ranges of MUF concentration and uses nonuniformly saturated membranes, reflecting the real distribution of enzyme activities in soil. The new calibration algorithm eliminated biases of the standard calibration and resulted in greater accuracy in predicting MUF concentrations. HighlightsWe developed a new approach to calibration for 2‐D soil zymography.The approach accounted for spatial nonuniformity of soil zymograms.Standard calibration resulted in systematic underestimation of enzyme activity.Soil zymography requires pixel‐based calibration with nonuniformly saturated membranes.