The excessive use of chemical fertilizers can lead to severe environmental damages. In recent decades, the application of biostimulants to improve soil composition and stimulate plant growth has contributed significantly to environmental preservation. In this paper, we studied the effect of a rhizogenic biostimulant, obtained from fulvic acids, probiotics, and prebiotics, on the fertility of two types of soils, sandy and sandy loam soils, in which turfgrass was growing. Soil samples from plots treated with biostimulant and controls (untreated plots) were collected. The analyzed parameters from the soil include organic matter, microbial activity, soil chemical composition, catalase, dehydrogenase, and phosphatase enzyme activities. Moreover, root lengths was examined and compared in turfgrass species. The biostimulant application improved microbial activity, organic matter, and enzymatic activity in both types of soils. The soil calcium, potassium, magnesium, and phosphorus content increased with the biostimulant application, whereas pH and electrical conductivity decreased. The most relevant improvement was a 77% increase of calcium for sandy loam soil and 38% increase in potassium for sandy soil. Biostimulant application led to a significant increase in turf root length. This increase was greater for sandy soil than in sandy loam soil with an increment of 43% and 34% respectively, compared to control.

Grasslands have a natural capacity to decrease air pollution and a positive impact on human life. However, their maintenance requires adequate irrigation, which is difficult to apply in many regions where drought and high temperatures are frequent. Therefore, the selection of grass species more tolerant to a lack of irrigation is a fundamental criterion for green space planification. This study compared responses to deficit irrigation of different turfgrass mixtures: a C₄ turfgrass mixture, Cynodon dactylon-Brachypodium distachyon (A), a C₄ turfgrass mixture, Buchloe dactyloides-Brachypodium distachyon (B), and a standard C₃ mixture formed by Lolium perenne-Festuca arundinacea-Poa pratensis (C). Three different irrigation regimes were assayed, full irrigated to 100% (FI-100), deficit irrigated to 75% (DI-75), and deficit irrigated to 50% (DI-50) of container capacity. Biomass, normalized difference vegetation index (NDVI), green area (GA), and greener area (GGA) vegetation indices were measured. Irrigation significantly affected the NDVI, biomass, GA, and GGA. The most severe condition in terms of decreasing biomass and vegetation indices was DI-50. Both mixtures (A) and (B) exhibited higher biomass, NDVI, GA, and GGA than the standard under deficit irrigation. This study highlights the superiority of (A) mixture under deficit irrigation, which showed similar values of biomass and vegetation indices under full irrigated and deficit irrigated (DI-75) container capacities.

The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture’s size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.

In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R² of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy.

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.

Soil moisture control is crucial to assess irrigation efficiency in green areas and agriculture. In this paper, we propose the design and calibration of a sensor based on inductive coils and electromagnetic fields. The proposed prototypes should meet a series of requirements such as low power consumption, low relative error, and a high voltage difference between the minimum and maximum moisture. We tested different prototypes based on two copper coils divided into two different sets (P1–P15 and NP1–NP4). The prototypes have different characteristics: variations in the number and distribution of spires, existence or absence of casing, and copper wires with a diameter of 0.4 or 0.6 mm. In the first set of experiments carried out in commercial soil, the results showed that the best prototypes were P5, P8, and P9. These prototypes were used in different types of soils, and P8 was selected for the subsequent tests. We carried the second set of experiments using soil from an agricultural field. Based on the data gathered, mathematical models for the calibration of prototypes were obtained and verified. In some cases, two equations were used for different moisture intervals in a single prototype. According to the verification results, NP2 is the best prototype for monitoring the moisture in agricultural lands. It presented a difference in induced voltage of 1.8 V, at 500 kHz, between wet and dry soil with a maximum voltage of 5.12 V. The verification of the calibration determined that the calibration using two mathematical models offers better results, with an average absolute error of 2.1% of moisture.