The influence of methods of basic tillage and fertilizers on the content of basic nutrients in leached chernozem

Relevance. The preservation, reproduction and rational use of agricultural soil fertility is the main condition for the stable development of the agro-industrial complex. Mechanical tillage systems, the use of mineral and microbiological fertilizers are one of the main links in adaptive landscape farming systems. In the conditions of a sharp decrease in the rates of fertilizer application, an increase in the imbalance of elements of mineral nutrition of plants observed in recent years in agroecosystems, the function of improving the regimes of chernozems, preserving their fertility is designed to perform resource-saving technologies of soil cultivation in combination with effective methods of using agrochemicals that combine environmental and economic feasibility.Methods. The experiments were laid in 2017–2019 on chernozem heavy loamy soils typical for most farms in the Ulyanovsk region. The object of the study is spring wheat, the variety Ulyanovskaya 100. The subject of the study is the methods of tillage, doses of mineral fertilizers, the biological product "BisolbiFit". The following technological methods of using the biological product were studied: seed treatment before sowing, non-root treatment of vegetating plants and a combination of these methods. The experiment was carried out on three backgrounds: N0P0K0 (control); 2) N30P30K30; 3) N60P60K60.Results. It was found that the best nitrification ability was possessed by variants with fine combback and comb-back with soil-deepening treatment, in which the weighted average content of nitrate nitrogen was 3.29–3.33 mg/100 g, which is 35–36%; 26–28%; 43–44% more than with fine, conventional non-dump and dump treatment respectively. Plowing improved the conditions of phosphorus and potassium nutrition of plants by 25–37% and 6–14% compared to other treatments. When N30P30K30 and N60 P60 K60 were applied to the soil, the content of nitrate nitrogen increased by 46 and 91%, phosphorus — by 0–14% and potassium — by 6 and 21% compared to the nonfertilized background. More effective in terms of the effect on the productivity of spring wheat were comb-shaped treatments, where the average yield was 2.89–2.94 t/ha, which exceeded the usual plowing by 0.19–0.24 t/ha. The greatest increase in yield was obtained when combining the methodsseed treatment + spraying of vegetative plants with the biological preparation "BisolbiFit". On an unfertilized background, the increase in grain yield was -0.71, on the background of N30P30K30 — 1.04, on the background of N60P60K60 — 1.56 t/ha.

Effects of Partial Blackwater Substitution on Soil Potential NI-Trogen Leaching in a Summer Maize Field on the North China Plain

In China, promoting harmless blackwater treatment and resource utilization in rural areas is a priority of the “toilet revolution”. Exploring the effects of blackwater application in arid areas on soil nitrogen losses can provide a basis for more effective water and fertilizer management. This study analyzed nitrogen leaching and maize yield under blackwater application in the summer maize season of 2020. A total of 5 treatments were used: no fertilizer, single chemical fertilizer application (CF), single blackwater application (HH), and combined chemical fertilizer and blackwater application ratios of 1:1 (CH1) and 2:1 (CH2). The total nitrogen leached from the fertilization treatments was 53.14–60.95 kg·ha−1 and the leached nitrate nitrogen was 34.10–40.62 kg·ha−1. Nitrate nitrogen accounted for 50–62% of the total leached nitrogen. Compared with blackwater treatments, nitrate nitrogen moved into deeper soil layers (80–100 cm depth) during the CF treatment. Compared with CF, HH significantly reduced the maize yield by 24.39%. The nitrogen surplus of HH was higher than that of other fertilizer treatments. Considering nitrogen leaching, maize yield, and economic benefits, the CH2 treatment presented the optimal results. These findings address knowledge gaps and assist in guiding policy-makers to effectively promote China’s “toilet revolution”.

The Effects of Concentration Gradient of Nitrogen Compounds on the Ammonium-nitrogen and Nitrate-nitrogen Fluxes at Sediment-Water Interface

The incubation experiments focused on altering concentration gradients of nitrogen between sediment and overlying water to examine the diffusion flux of ammonium-nitrogen (NH4+) and nitrate-nitrogen (NO3-) at sediment-water interface. In this study, the diffusion flux can be estimated based on calculating the average of the net change rate of nutrient concentrations in the overlying water. For the incubation experiment of different TN concentrations in the sediment, the results showed that the diffusion flux of ammonia at sediment-water interface is -52.57~84.57 mg·m-2·d-1, and for nitrate diffusion flux, the changing range during the incubation experiment is -110.13~143.25 mg·m-2·d-1. For the incubation experiment of different nitrogen concentrations in the overlying water, the results of NH4+-N diffusion flux in L, M, H treatment were 3.37, -4.94, -3.84 mg·m-2·d-1, respectively. And the average diffusion flux of nitrate in L (0 mg NO3--N, 0 mg NH4+-N), M (0.5 mg NO3--N, 1.5 mg NH4+-N) and H (1 mg NO3--N, 2.5 mg NH4+-N) treatment were 12.30, 10.39 and 7.11 mg·m-2·d-1. Results highlighted that concentrations gradient of nutrients were indeed an important factor affecting the diffusion flux at sediment-water interface. In addition, the diffusion of nutrients at sediment-water interface in aquatic ecosystem is not only controlled by concentration gradients, some other factors such as incoming water, hydrodynamics, dissolved oxygen content, sediment structure, biological disturbance, horizontal migration and diffusion of nutrients and turbulent diffusion caused by wind and wave, are equally important.

Effects of Biochar Application on Soil Nitrogen Conversion and Microbial Community Composition

In northeastern China, successive years of cultivation have led to a decline in soil quality, a process that is exacerbated by the over-application of chemical fertilizers to ensure staple food production. The large amount of straw produced by cultivation is difficult to effectively use in recent years. There has been an increasing amount of research on the transforming straw into biomass char, but it has often focused on the effects of biomass char addition on soil physicochemical properties, without further exploring the mechanisms of this process and its effects on soil microorganisms. Microorganisms are an important part of the soil system and the process of how biomass char addition affects microorganisms through its effect on soil physicochemical properties should not be overlooked. In this study， the effect of biochar application at different preparation temperatures (300°C, 400°C and 500°C) and addition contents (0.1% and 1%) on ammonia, nitrate and total nitrogen in soil leachates were investigated. The effect of microbial sequencing on the dynamics of carbon and nitrogen was also investigated to reveal the mechanisms contributing to the changes in nitrogen forms. The results showed that biochar had a better adsorption ability on ammonia nitrogen, and biochar promoted the conversion of ammonia nitrogen to nitrate nitrogen by nitrifying bacteria. The addition of 1% biochar (prepared at 500°C) increased nitrate-nitrogen leaching by 86.52% compared to the control treatment. The sequencing of microorganisms also revealed that biochar changed the structure and abundance of the soil microbial community, especially increasing the relative abundance of the Helicobacter nitrification phylum by 2.02%. These results indicates that biochar facilitated the adsorption of ammonium nitrogen and the conversion of nitrate nitrogen, and solving the problem of low nitrogen fertilizer utilization while promoting the formation of beneficial bacteria in the soil.

Ammonia–Nitrate Mixture Dominated by NH4+–N Promoted Growth, Photosynthesis and Nutrient Accumulation in Pecan (Carya illinoinensis)

Although ammonia–nitrogen (NH4+–N) and nitrate–nitrogen (NO3−–N) are the two main forms of N absorbed and utilized by plants, the preferences of plants for these forms are still unclear. In this study, we analyzed the growth, photosynthesis, and nutrients of pecan under different NH4+:NO3− ratios (0/0, 0/100, 25/75, 50/50, 75/25, 100/0) by indoor aerosol incubation. The results showed that additions of different N forms promoted the growth and development of pecan seedlings. When NO3−–N was used as the sole N source, it significantly promoted the ground diameter growth of pecan and increased the leaf pigment content and photosynthetic rate. The NH4+:NO3− ratio of 75:25 and NH4+–N as the sole N source significantly increased the soluble sugars in stems and roots, starch in leaves, stems and roots, soluble protein in leaves and stems, and soluble phenols in stems and roots. Additionally, the NH4+:NO3− ratio of 75:25 increased plant height, leaf number, root soluble protein, and leaf soluble phenol contents. In conclusion, regarding the physiological aspects of pecan growth, pecans are more inclined to use NH4+–N. Considering that the NH4+–N as the only N source may lead to nutrient imbalance or even toxicity, the NH4+:NO3− ratio of 75:25 was most favorable for the growth and development of pecan seedlings.

A Literature Review of Wetland Treatment Systems Used to Treat Runoff Mixtures Containing Antibiotics and Pesticides from Urban and Agricultural Landscapes

Wetland treatment systems are used extensively across the world to mitigate surface runoff. While wetland treatment for nitrogen mitigation has been comprehensively reviewed, the implications of common-use pesticides and antibiotics on nitrogen reduction remain relatively unreviewed. Therefore, this review seeks to comprehensively assess the removal of commonly used pesticides and antibiotics and their implications for nitrogen removal in wetland treatment systems receiving non-point source runoff from urban and agricultural landscapes. A total of 181 primary studies were identified spanning 37 countries. Most of the reviewed publications studied pesticides (n = 153) entering wetlands systems, while antibiotics (n = 29) had fewer publications. Even fewer publications reviewed the impact of influent mixtures on nitrogen removal processes in wetlands (n = 16). Removal efficiencies for antibiotics (35–100%), pesticides (−619–100%), and nitrate-nitrogen (−113–100%) varied widely across the studies, with pesticides and antibiotics impacting microbial communities, the presence and type of vegetation, timing, and hydrology in wetland ecosystems. However, implications for the nitrogen cycle were dependent on the specific emerging contaminant present. A significant knowledge gap remains in how wetland treatment systems are used to treat non-point source mixtures that contain nutrients, pesticides, and antibiotics, resulting in an unknown regarding nitrogen removal efficiency as runoff contaminant mixtures evolve.

Reverse Osmosis Treatment of Wastewater for Reuse as Process Water—A Case Study

The aim of this work was to purify mixed wastewater from three different production processes in such a manner that they could be reused as process water. The maximum allowed concentrations (MAC) from the Environmental Standards for emissions of substances released into surface water were set as target concentrations. Wastewaters contained solid particles, sodium, aluminium, chloride, and nitrogen in high amounts. Quantitatively, most wastewaters were generated in the production line of alumina washing. The second type of wastewater was generated from the production line of boehmite. The third type of wastewater was from regeneration of ion exchangers, which are applied for feed boiler water treatment. The initial treatment step of wastewater mixture was neutralisation, using 35% HCl. The precoat filtration followed, and the level of suspended solids was reduced from 320 mg/L to only 9 mg/L. The concentrations of ions, such as aluminium, sodium and chlorides remained above the MAC. Therefore, laboratory reverse osmosis was applied to remove the listed pollutants from the water. We succeeded in removal of all the pollutants. The concentration of aluminium decreased below 3 mg/L, the sodium to 145 mg/L and chlorides to 193 mg/L. The concentration of nitrate nitrogen decreased below 20 mg/L.

Prediction of nitrate nitrogen content in soil using machine learning

The possibilities and feasibility of using the Bayesian network of trust and logistic regression to predict the content of nitrate nitrogen in the 0-40 cm soil layer before sowing have been investigated. Data from long-term multifactor field experience at the Siberian Research Institute of Farming and Agricultural Chemization of SFSCA RAS for 2013-2018 were used to train the models. The experiment was established on leached chernozem in the central forest-steppe subzone in 1981 in the Novosibirsk region. Considering the characteristics of the statistical sample (observation and analysis data), the main predictors of the models affecting nitrate nitrogen content in soil were identified. The Bayesian trust network is constructed as an acyclic graph, in which the main (basic) nodes and their relationships are denoted. Network nodes are represented by qualitative and quantitative plot parameters (soil subtype, forecrop, tillage, weather conditions) with corresponding gradations (events). The network assigns a posteriori probability of events for the target node (nitrate-nitrogen content in the 0-40 cm soil layer) as a result of experts completing the conditional probability table, taking into account the analysis of empirical data. Two scenarios were analyzed to test the sustainability of the network and satisfactory results were obtained. The result of the logistic regression is the coefficients characterizing the closeness of the relationship between the dependent variable and the predictors. The coefficient of determination of the logistic regression is 0.7. This indicates that the quality of the model can be considered acceptable for forecasting. A comparative assessment of the predictive capabilities of the trained models is given. The overall proportion of correct predictions for the Bayesian confidence network is 84%, for logistic regression it is 87%.