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Highlights Changes to soil properties and precipitation scenarios significantly affect the water balance in agro-hydrology. SPAW model is sensitive to simulated runoff and infiltration, but it has limitations in responding to soil compaction and organic matter change. Increasing organic matter (1% to 5%) did not significantly affect runoff or infiltration in silty and sandy loam soil. Low precipitation generates significantly lower runoff (%) and higher infiltration. Abstract. Agricultural practices can change soil properties and the amount of runoff generated from a landscape. Modeling results could be significantly different than expected if the web soil survey or other commonly used remote sensing applications are used as model inputs without site verification. This study assessed the applicability and sensitivity of the Soil-Plant-Air-Water (SPAW) Model for simulating the runoff (%) and infiltration (%) components of the water balance for various soil physical properties, cover crop, and weather variables. Soil profiles in 135 combinations were developed with three soil classes (sandy loam, silt loam, and clay), five organic matter levels (1%, 2%, 3%, 4%, and 5%), three levels of compaction (low, medium, and high), and three topsoil layer thicknesses (7.6 cm, 11.4 cm, and 15 cm). Also, three cover crop treatments were simulated by modifying surface cover and evapotranspiration during the non-growing season. Finally, two precipitation regimes were considered (Iowa City, IA, as high precipitation and Brookings, SD, as low precipitation) to simulate runoff and infiltration. In total, 810 scenarios were run, resulting in over 300 million data points. This study confirmed that soil texture, bulk density, and topsoil thickness significantly (p<0.01) influence runoff generation and infiltration percentage based on the water balance criterion. Interestingly, the SPAW model had no significant response on runoff (%) and infiltration (%) to organic matter levels changing from 1% to 5%. This simulation demonstrates that runoff estimations can be significantly influenced by soil properties that can change due to agricultural conservation practices (ACPs) or, conversely, by compaction events. Inputs to models must account for these changes rather than relying only on historical or remote sensing inputs. Keywords: Agricultural conservation practices, Conservation agriculture, Field hydrology, Infiltration, Runoff, SPAW. 

Abstract The use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.