Application of Biostimulant in Seeds and Soil on Three Chickpea Varieties: Impacts on Germination, Vegetative Development, and Bacterial Facilitation of Nitrogen and Phosphorus.

Chickpeas (Cicer arietinum L.) are a valuable legume crop due to their nutritional value. To maintain chickpea productivity and avoid the adverse effects of climate change on soil and plant processes, it is crucial to address demand. Achieving this necessitates implementing sustainable agricultural practices incorporating the use of biostimulants, adaptable crops for arid conditions, as well as pest and disease-resistant crops that are sustainable over time. Three varieties of chickpeas were analysed to determine the effect of two different biostimulant application methods on both germination and vegetative growth. Possible effects due to location were also examined by conducting tests at two different sites. Significant variations in biostimulant response were evident only during the germination period, but not during the vegetative development stage, where the observed statistical differences were influenced more by the location or variety of chickpeas employed. Furthermore, this study examined the effect of biostimulants on nutrient cycling within the soil-plant microbiota system. Nitrogen-fixing bacteria (NFB) are present in the soil of chickpea crops at an order of magnitude of 107 CFU/g DS. Additionally, an average concentration of 106 CFU/g DS of phosphorus-mobilising bacteria was observed. Applying biostimulants (BioE) to seeds resulted in a successful germination percentage (GP) for both Amelia (AM) and IMIDRA 10 (IM) varieties.

Fate of microplastics in agricultural soils amended with sewage sludge: Is surface water runoff a relevant environmental pathway?

Sewage sludge used as agricultural fertilizer has been identified as an important source of microplastics (MPs) to the environment. However, the fate of MPs added to agricultural soils is largely unknown. This study investigated the fate of MPs in agricultural soils amended with sewage sludge and the role of surface water runoff as a mechanism driving their transfer to aquatic ecosystems. This was assessed using three experimental plots located in a semi-arid area of Central Spain, which were planted with barley. The experimental plots received the following treatments: (1) control or no sludge application; (2) historical sludge application, five years prior to the experiment; and (3) sludge application at the beginning of the experiment. MPs were analyzed in surface water runoff and in different soil layers to investigate transport and infiltration for one year. The sewage sludge used in our experiment contained 5972-7771 MPs/kg dw. Based on this, we estimated that about 16,000 MPs were added to the agricultural plot amended with sludge. As expected, the sludge application significantly increased the MP concentration in soils. The control plot contained low MP concentrations (31-120 MPs kg-1 dw), potentially originating from atmospheric deposition. The plot treated five years prior to the experiment contained 226-412 and 177-235 MPs kg-1 dw at the start and end of the experiment, respectively; while the recently treated plot contained 182-231 and 138-288 MPs kg-1 dw. Our study shows that MP concentrations remain relatively constant in agricultural soils and that the MP infiltration capacity is very low. Surface water runoff had a negligible influence on the export of MPs from agricultural soils, mobilizing only 0.2-0.4% of the MPs added with sludge. We conclude that, in semi-arid regions, agricultural soils can be considered as long-term accumulators of MPs.

Evaluating the Effects of Environmental Conditions on Sensed Parameters for Green Areas Monitoring and Smart Irrigation Systems.

The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities.

Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas.

Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.