Search

Composting is a nature-based method used to stabilize organic matter and to transform nitrogen from animal farm manure or solid fraction of slurry (SFS). The use of composted material as source of nutrients for agriculture is limited by its potential to facilitate the propagation of biological hazards like pathogens and antibiotic-resistant bacteria and their associated antibiotic-resistance genes (ARG). We show here an experimental on-farm composting (one single batch) of pig SFS, performed under realistic conditions (under dry continental Mediterranean climate) for 280 days, and using two different bulking agents (maize straw and tree pruning residues) for the initial mixtures. The observed reduction in potentially pathogenic bacteria (80-90%) and of ARG loads (60-100%) appeared to be linked to variations in the microbiome composition occurring during the first 4 months of composting, and concurrent with the reduction of water-soluble ammonium and organic matter loads. Nitrification during the composting has also been observed for both composting piles. Similar patterns have been demonstrated at small scale and the present study stresses the fact that the removal can also occur at full scale. The results suggest that adequate composition of the starting material may accelerate the composting process and improve its global performance. While the results confirm the sanitization potential of composting, they also issue a warning to limit ARG loads in soils and in animal and human gut microbiomes, as the only way to limit their presence in foodstuffs and, therefore, to reduce consumers' exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Among the diverse techniques for monitoring soil moisture, capacitance-type soil moisture sensors are popular because of their low cost, low maintenance requirements, and acceptable performance. However, although in laboratory conditions the accuracy of these sensors is good, when installed in the field they tend to show large sensor-to-sensor differences, especially under drip irrigation. It makes difficult to decide in which positions the sensors are installed and the interpretation of the recorded data. The aim of this paper is to study the variability involved in the measurement of soil moisture by capacitance sensors in a drip-irrigated orchard and, using this information, find ways to optimize their usage to manage irrigation. For this purpose, the study examines the uncertainties in the measurement process plus the natural variability in the actual soil water dynamics. Measurements were collected by 57 sensors, located at 10 combinations of depth and position relative to the dripper. Our results showed large sensor-to-sensor differences, even when installed at equivalent depth and coordinates relative to the drippers. In contrast, differences among virtual sensors simulated using a HYDRUS-3D model at those soil locations were one order of magnitude smaller. Our results highlight, as a possible cause for the sensor-to-sensor differences in the measurements by capacitance sensors, the natural variability in size, shape, and centering of the wet area below the drippers, combined with the sharply defined variation in water content at the soil scale perceived by the sensors.

Recent technological advances have made possible automated irrigation scheduling using decision-support tools. These tools help farmers to make better decisions in the management of their irrigation system, thus increasing yields while preserving water resources. The aim of this study is to evaluate in a commercial plot an automated irrigation system combined with remote-sensing techniques and soil mapping that allows the establishment of regulated deficit irrigation (RDI) strategies. The study was carried out over 3 years (2015-2017) in a commercial hedgerow olive orchard of the variety 'Arbequina' located in Alvarado (Extremadura, Spain). An apparent electrical conductivity (ECa) map and a normalized difference vegetation index (NDVI) map were generated to characterize the spatial variability of the plot and classify the zones in homogeneous areas. Then, reference points were selected to monitor the different irrigation sectors. In 2015, the plot was irrigated according to the farmer's technical criteria throughout the plot. In 2016 and 2017, two different areas of the plot were irrigated applying an RDI strategy, one under expert supervision and the other automatically. The results show that in a heterogeneous plot the use of new technologies can be useful to establish the ideal location for an automatic irrigation system. Furthermore, automatic irrigation scheduling made it possible to establish an RDI strategy recommended by an expert, resulting in the homogenization of production throughout the plot without the need for human intervention.

Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach.

Affordable and easy-to-use methods for assessing biomass and leaf area index (LAI) would be of interest in most breeding programs. Here, we describe the evaluation of a protocol for photographic sampling and image analysis aimed at providing low-labor yet robust indicators of biomass and LAI. In this trial, two genotypes of triticale, two of bread wheat, and four of tritordeum were studied. At six dates during the growing cycle, biomass and LAI were measured destructively, and digital photography was taken on the same dates. Several vegetation indices were calculated from each image. The results showed that repeatable and consistent values of the indices were obtained in consecutive photographic samplings on the same plots. The photographic indices were highly correlated with the destructive measurements, though the magnitude of the correlation was lower after anthesis. This work shows that photographic assessment of biomass and LAI can be fast, affordable, have good repeatability, and can be used under bright and overcast skies. A practical vegetation index derived from pictures is the fraction of green pixels over the total pixels of the image, and as it shows good correlations with all biomass variables, is the most robust to lighting conditions and has easy interpretation., (© 2013 Institute of Botany, Chinese Academy of Sciences.)

This work investigates the use of electronic tongues for monitoring nutrient solution compositions in closed soilless systems. This is a horticultural technique in which the nutrient solution is continuously recirculated and an automatic recomposition system maintains the concentration of the different ions in the optimum range for the plants. Electronic tongues used in this study comprised an array of potentiometric sensors and complex data processing by artificial neural networks. A first experiment was able to carry out the simultaneous inline monitoring of ammonium, potassium, sodium, chloride, and nitrate ions during the winter. In the second and third applications, done during summer, some changes were introduced in the sensor array to improve its response toward chloride ions and to incorporate phosphate in the model. This electronic tongue was validated with real greenhouse samples and was also able to detect the variations in the ion concentrations caused by an incorrect configuration of the recomposition system.