Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

In the US upper Midwest, the narrow growing season causes many farmers to presume yield losses when reducing tillage. The purpose of this study was to determine how four production-scale tillage systems affected residue cover, stand populations, crop yields, and soil chemical, biological, and physical properties. Tillage systems (chisel plow, fall strip-till with shanks, spring strip-till with coulters, and shallow vertical till) were continued for 4 yr. Tillage effects within a site were few and mixed (0.17-0.36 Mg ha-1 difference), whereas site effects were common (0.50-3.00 Mg ha-1 difference). Among 19 soil properties, only fungal/bacteria ratios differed among strip-till with shanks (0.078) and strip-till with coulters (0.066) at one site. Our results suggest that many farmers' concerns about using conservation tillage practices do not necessarily translate into yield losses when compared to standard chisel plow practices. Economics and the level of erosion control among the tillage practices compared here, rather than yield alone, should guide farmer preferences.

Core Ideas Hydromulch was applied to disturbed, bare soil at two rates. Micro‐Bowen ratio systems quantified soil temperature and evaporation. Hydromulch moderated temperature fluctuations, especially daily maximums. Hydromulch application reduced evaporation following rainfall. North Dakota State Univ., Dep. of Soil Science, Fargo, ND 58108. Soil disturbance reduces plant‐residue cover and can leave bare soil susceptible to erosion, extreme temperature fluctuations, and increased evaporation. Under such conditions, establishing vegetation is difficult. To overcome these difficulties, managing disturbed lands by applying surface cover may be a good step toward soil reclamation. Hydromulch is often applied to stabilize soil after disturbance, but its influence on soil temperature and evaporation has not been described. This study assessed soil temperature over time and used the surface energy balance to quantify evaporation from bare soil (0×), and two rates of hydromulch application, 1× and 3× (by weight) of manufacturer's recommended rate. Diurnal temperature extremes were highest in the 0× and least in the 3×. Evaporation was highest in the 0× during the final 18 d of data collection and lowest in the 3×. These findings indicate that temperature fluctuations decrease and evaporation is reduced when hydromulch is applied to bare soil, suggesting it may aid in soil reclamation.

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