Current Methods, Common Practices, and Perspectives in Tracking and Monitoring Bioinoculants in Soil

Microorganisms promised to lead the bio-based revolution for a more sustainable agriculture. Beneficial microorganisms could be a valid alternative to the use of chemical fertilizers or pesticides. However, the increasing use of microbial inoculants is also raising several questions about their efficacy and their effects on the autochthonous soil microorganisms. There are two major issues on the application of bioinoculants to soil: (i) their detection in soil, and the analysis of their persistence and fate; (ii) the monitoring of the impact of the introduced bioinoculant on native soil microbial communities. This review explores the strategies and methods that can be applied to the detection of microbial inoculants and to soil monitoring. The discussion includes a comprehensive critical assessment of the available tools, based on morpho-phenological, molecular, and microscopic analyses. The prospects for future development of protocols for regulatory or commercial purposes are also discussed, underlining the need for a multi-method (polyphasic) approach to ensure the necessary level of discrimination required to track and monitor bioinoculants in soil.

Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2>0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

An Evaluation of a New Scheme for Determination of Irrigation Depths in the Egyptian Nile Delta

Innovative irrigation techniques should be implemented to improve irrigation management in dryland countries. In this regard, a new scheme, that uses three sets of irrigation depth and numerically simulated cumulative transpiration, was evaluated in the Egyptian Nile delta in 2020. Presuming that water is volumetrically priced, the proposed scheme can maximize net incomes at optimum irrigation depths considering quantitative weather forecasts. A field experiment was carried out with a randomized complete block design using a major crop, maize, to assess the feasibility of the proposed scheme in comparison to a sensor-based irrigation method under conditions of dry climate and clay loamy soil. The proposed scheme could increase the gross net income of farmers and conserve irrigation by 21% and 35%, respectively, compared to a sensor-based irrigation method, although the yield and its components were almost the same with no significant statistical differences. The model could accurately simulate soil water content in the topsoil layers with a RMSE of 0.02 cm3 cm−3. The proposed scheme could be a useful tool to spare the costs of expensive soil monitoring sensors while saving water and improving net income.

Arsenic in Agricultural Soils of Slovakia

<p>The article presents the current distribution of arsenic in agricultural soils of Slovakia. The current concentration of arsenic (extracted with <em>aqua regia</em>) was measured and evaluated based on 318 monitoring sites of national soil monitoring system in Slovakia. Based on the obtained results, one can state that the average content of arsenic is lower than the valid hygienic limit for arsenic (25 mg.kg-1) for predominated sandy-loamy and loamy soils in Slovakia. Increased values of arsenic were determined only for the Horná – Upper Nitra region (anthropogenic impact) – 24.5 mg.kg-1 and for the Stredný – Central Spiš region (mixed anthrophogenic and geogenic impact) – 129.5 mg.kg-1. These regions belong to the most arsenic-affected regions in Slovakia, where the content of bioavailable forms of arsenic is also increased in the range of 0.013–0.997 mg.kg-1. The hygienic limit for bioavailable arsenic in soils of Slovakia is 0.4 mg.kg-1. Finally, there is a serious risk of arsenic transport from soil into the plants and food chain especially in case of acid soils. A higher risk of As presence seems to be in anthropogenically affected soils.</p>

LOCALIZATION OF SOIL MONITORING DATA PROCESSING

A tree of data analysis for the formation and preprocessing, storage and protection of data based on Big Data and Blockchain technologies has been developed. The developed algorithm allows for the classification of data on the state of the field, split testing of data, forecasting and machine learning for the implementation of differential irrigation with sprinklers.

Soil Monitoring and Testing using IoT for Fertility Level and Crop Prediction

Agribusiness is the foundation of India. In India, 50 % of the remaining task at hand depends upon agribusiness. The responsibility of the agriculture part in the Indian economy is higher than some other divisions in India. In any case, Farmers utilized customary strategy for developing harvests, which involve less profitability of yields. Additionally, an erosion and disintegration are likewise a principle motivation to less profitability of yields. This will impact in diminishes fruitfulness level. Loss of soil supplements through different courses is likewise motivation to diminish soil richness level. Supplements like potassium (K), nitrogen (N), and phosphorus (P) are basic for the evolution of a plant. The advancement in agriculture is vital to tackle these issues in the agribusiness part and shrewd cultivating is that the appropriate response. This can be conceivable utilizing IOT gadgets. Farmers can get the necessary data just as the screen is yield. IOT associates the whole world with the help of sensors and other installed gadgets. The diverse soil tests will be taken from various fields and soil esteems will be determined to utilize a PH sensor where supplements worth will be separated from it. The live information will be sent to the cloud database where information will be put away. Where information will be broke down to improve crop yields. Here for information broke down information mining method is used. Information mining in agriculture assumes an imperative job in yield assumptions, soil productivity, and plant sicknesses and so on. Farmers can watch information on screen by a website. What's more, farmers will likewise get crop lists dependent on that information which harvests will be possible to yield in that soil.