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AnnAGNPS is a watershed-scale hydrological model designed to analyze the impact of nonpoint source pollution in agricultural environments. Its unique capabilities have defined it as an essential model that is used globally to evaluate agricultural management scenarios. However, it does not currently have a user-friendly graphical interface that provides a simple way for users to perform simulations. This article presents QAnnAGNPS, a plugin developed in QGIS to facilitate access to the simulation capabilities of AnnAGNPS through a user-friendly interface and the addition of extra features, including data visualization. QAnnAGNPS, in addition to fulfilling this valuable task, opens the door to the incorporation of additional functions already included in other similar hydrological models. The plugin has been tested in the Latxaga cereal basin in Navarra, Spain, and has demonstrated that it provides a simpler way to perform simulations and visualize results compared to AnnAGNPS. • The QAnnAGNPS plugin has been developed to integrate the AnnAGNPS model into QGIS. • QAnnAGNPS allows using AnnAGNPS model capabilities in an easier way than before. • QAnnAGNPS adds new functionalities to AnnAGNPS, facilitating the whole process. • QAnnAGNPS brings the capabilities of AnnAGNPS to a wider range of users. • QAnnAGNPS opens many possibilities of adding new functionalities. [ABSTRACT FROM AUTHOR]

Agricultural activities contribute to water pollution through sediments and nutrient export, negatively affecting water quality and aquatic ecosystems. However, implementing best management practices (BMPs) could help control sediments and nutrient losses from agricultural catchments. This study used the Soil Water Assessment Tool (SWAT) model to assess the effectiveness of four BMPs in reducing sediment and phosphorus export in a small agricultural catchment (33 km2) in southeastern Sweden. The SWAT model was first evaluated for its ability to simulate streamflow, sediment load, and total phosphorous load from 2005 to 2020. Then, the calibrated parameters were used to simulate the agricultural BMP scenarios by modifying relevant parameters. The model performed satisfactorily during calibration and validation for streamflow (NSE = 0.80/0.84), sediment load (NSE = 0.67/0.69), and total phosphorous load (NSE = 0.61/0.62), indicating its suitability for this study. The results demonstrate varying effects of BMP implementation on sediment and phosphorus (soluble and total) export, with no significant change in streamflow. Filter strips were highly effective in reducing sediment (−32%), soluble phosphorus (−67%), and total phosphorous (−66%) exports, followed by sedimentation ponds with −35%, −36%, and −50% reductions, respectively. Grassed waterways and no-tillage were less impactful on pollutant reduction, with grassed waterways showing a slight increase (+4%) in soluble phosphorus and no-tillage having a minimal effect on sediment (−1.3%) and total phosphorus (−0.2%) export. These findings contribute to the ongoing efforts to mitigate sediment and nutrient pollution in Swedish agricultural areas, thereby supporting the conservation and restoration of aquatic ecosystems, and enhancing sustainable agricultural practices. • Filter strips and sedimentation ponds were highly effective in pollutant reduction. • Grassed waterways and no-tillage had less impact on pollutant reduction. • Filter strip effectiveness in pollutant reduction increased with increasing width. • Total phosphorous export from the catchment is dependent on sediment export. • The analyzed BMPs had no significant impact on streamflow. [ABSTRACT FROM AUTHOR]

Predicting water quality and quantity response to climate change in a watershed is very difficult due to the complexity and uncertainties in estimating and understanding future hydrological conditions. However, hydrological models could simplify the processes and predict future impacts of agricultural activities. This study aimed to evaluate the applicability of the Soil Water Assessment Tool (SWAT) model for climate change prediction of streamflow and nitrate load in an agricultural Mediterranean watershed in northern Spain. The model was first evaluated for simulating streamflow and nitrate load under rainfed agricultural conditions in the Cidacos River watershed in Navarre, Spain. Then, climate change impact analysis on streamflow and nitrate load was conducted in the short-term (2011–2040), medium-term (2041–2070), and long-term (2071–2100) future projections relative to the historical baseline period (1971–2000) under the RCP4.5 and RCP8.5 CO 2 emission scenarios. The model evaluation showed a good model performance result during calibration (2000–2010) and validation (2011–2020) for streamflow (NSE = 0.82/0.83) and nitrate load (NSE = 0.71/0.68), indicating its suitability for adoption in the watershed. The climate change projection results showed a steady decline in streamflow and nitrate load for RCP4.5 and RCP8.5 in all projections, with the long-term projection scenario of RCP8.5 greatly affected. Autumn and winter saw a considerable drop in comparison to spring and summer. The decline in streamflow was attributed to the projected decrease in precipitation and increase in temperatures, while the nitrate load decline was consistent with the projected streamflow decline. Based on these projections, the long-term projection scenarios of RCP8.5 indicate dire situations requiring urgent policy changes and management interventions to minimize and mitigate the resulting climate change effects. Therefore, adapted agricultural management practices are needed to ensure sustainable water resource utilization and efficient nitrogen fertilizer application rates in the watershed to reduce pollution. [Display omitted] • The SWAT model was used to simulate streamflow and nitrate load response to climate change. • The key climate change drivers were declining precipitation and rising temperature. • Decrease in future streamflow and nitrate load, particularly in the long-term RCP8.5 projection. • Climate change affects agriculture through changes in phenology and cropping cycle. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management. [Display omitted] • Soil moisture was estimated in rain-fed and irrigated wheat fields using Sentinel-1 data. • Three change detection methods were evaluated. • Different methodological alternatives were proposed for applying change detection methods at the field scale. • The results of two methods improved when correcting for the vegetation influence on backscatter. • Soil moisture was generally overestimated for dry conditions. [ABSTRACT FROM AUTHOR]

• Large-scale farm monitoring assessment of LPWAN with tractor/farmer interaction. • Wireless channel analysis of multiple communication links in the farming scenario. • Coverage/capacity analysis enhancing equipment performance and resource management. • LPWAN-based WSNs provide in general enhanced communication performance. • System validation, providing flexible/ scalable solutions for interactive farming. In this paper, the assessment of multiple scenario cases for large-scale farm monitoring using Low-Power Wide-Area Network (LPWAN) based near-ground sensor nodes with the interaction of both tractors and farmers are presented. The proposed scenario under analysis considers multiple communication links, namely nodes to infrastructure, nodes to tractor, nodes to farmer, tractor to infrastructure and farmer to infrastructure communications. Moreover, these scenarios are proposed for tractors and agricultural equipment performance improvement and tracking, as well as resources management within the farm field. Different link type configurations are tested in order to consider the impact of ground, spatial distribution as well as infrastructure elements. The results show that LPWAN-based WSNs can provide better performance in terms of coverage and radio link quality results than ZigBee for a non-flat large-scale farm field in both cases of near-ground fixed nodes and moving tractor and farmer. The proposed systems are validated by cloud-based platforms for LoRaWAN, Sigfox and NB-IoT communications, providing flexible and scalable solutions to enable interactive farming applications. [ABSTRACT FROM AUTHOR]