Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Automated soil moisture systems are commonly used in precision agriculture and environmental monitoring. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 under lab and field conditions. The laboratory tests aimed at evaluating the response speed, best supply voltage, temperature dependency, calibration, and applicability for controlled infiltration column tests (one meter high). Laboratory tests indicated that the sensor is temperature and voltage-sensitive. The use of 5.5 V as supply voltage for the sensors drastically reduced the correlation between output and degree of soil saturation, thus we suggest the use of 3.3V. Soil temperature had a negligible impact on the sensor output: 0.27% of soil saturation degree per degree Celsius. For field implementation, a low-cost monitoring station was built using Arduino as a microcontroller and tested during three months. The sensors could represent daily and seasonal oscillation in soil moisture resulting from solar heating and precipitation. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor's response in dry soil. The monitored wetting front was compared to the one estimated by Hydrus model and had a high correlation. The low-cost sensor performance was later compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors promote soil moisture measurements with high accuracy at a high acquisition cost. On the other hand, low-cost sensors, such as the SKU:SEN0193, provide data with medium accuracy at a very low acquisition cost, enabling spatial monitoring through multiple-point measurements. Thus, the use of the SKU:SEN0193 sensor is suggested in projects with budget limitations with short duration where there is a medium requirement accuracy or when the spatial variability of soil water content is considerable. Despite the physical fragility of the hardware used (sensors and monitoring station) and the lower accuracy when compared to other commercial sensors, this work demonstrates through the case study of the SKU:SEN0193 sensor the possibility of using low-cost technologies for monitoring environmental variables.

The curve number (CN) method is the most widely used method to estimate the depth of runoff resulting from a given depth of rainfall. However, there are still doubts about the effect of rainfall characteristics and soil moisture conditions on the method’s parameters (CN, and the standard initial abstraction ratio, $\lambda$ ). In this study, design rainfall distributions were used along with Hortonian infiltration characteristics to calculate CN and $\lambda$ values for a highly clayey soil ($>80\%$ ). Prerainfall infiltration conditions were characterized by the measured initial soil moisture content, and Hortonian infiltration model was obtained from experimental infiltration test measurements. The main results show that there is no influence of initial infiltration rates on CN values, and rainfall duration generates substantial influence on CN. The CN is inversely related to rainfall depth, and $\lambda$ is dependent on the initial soil moisture. Further, neither tabulated CN nor standard $\lambda$ corresponds to the simulated values obtained in this case. Therefore, these findings suggest that the method’s parameters should be specifically adapted to local hydrological conditions.

The Brazilian Savanna (Cerrado) is the second‐largest South American biome that corresponds to almost two‐third of the national agricultural production. Extensive agricultural‐driven land‐use changes have significantly altered the landscape, causing increased soil erosion. Furthermore, projections of climate change effects on the Cerrado raise concerns about the potential exacerbation of soil loss and its consequences on ecosystem sustainability. This study investigated soil loss for the Cerrado ecosystem by assessing current rates and projecting the potential effects of future climate change. Current soil loss was based on experimental plots (100 m²) during 7 years maintained under typical main land cover in Brazil (sugarcane, pasture, Cerrado, and bare soil). Erosivity, by using the Universal Soil Loss Equation (USLE), was estimated from observations, parameters of erodibility, and land cover. To assess the future soil loss (2100), we used the calibrated USLE equation with yearly erosivity derived from 12 downscaled and bias‐corrected SSP2‐4.5 and SSP5‐8.5 scenarios of CMIP6 climate model projections. Current agricultural practices induce considerable erosion, where sugarcane has 3.4 times higher soil loss as compared with the natural soil cover. Regarding future SSP2‐4.5 and SSP5‐8.5 scenarios (2100), we estimated an increase of 4.9% and 7.6% in soil loss, respectively, for all land covers. The observed soil loss rates underscore the critical importance of implementing sustainable land management practices to mitigate further soil degradation. Climate change may impose additional stress on the Cerrado ecosystem, amplifying the urgency for adaptive measures to safeguard this important biome.