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Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Soil and Plant Nutrition".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 18325

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Special Issue Editors

Dr. Xuguang Xing

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Guest Editor

Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Xianyang, China
Interests: abiotic stress; agricultural water saving; microplastic; nutrient cycling; soil water and salt
Special Issues, Collections and Topics in MDPI journals

Dr. Ankit Garg

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Guest Editor

Department of Civil and Smart Construction Engineering, Guangdong Engineering Centre for Structure Safety and Health Monitoring, Shantou University, Jinping District, Shantou 515063, China
Interests: biochar; tillage; unsaturated soil; soil–biochar–plant interactions; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Long Zhao

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Guest Editor

College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Interests: artificial intelligence; evapotranspiration; crop modeling; machine learning; precision agriculture; remote sensing

Special Issue Information

Dear Colleagues,

The interaction of soil and plants plays an important role in water and nutrient dynamics in an ecosystem. The function of nutrients, biomass, and water recycling is important for social, economic, and environmental benefits and also for providing various ecosystem services. Soil quality in the rhizosphere promotes vegetation growth and helps to maintain the functionality of the soil–plant system. The soil–plant system is likely to be damaged due to weak agricultural integrated managements. In order to tackle this, sustainable and intelligent agriculture is promoted that involves new methods of remediation of soil conditions and crop modeling; however, their feasibility in field conditions needs further development. The development of new biomaterials that are economical and also feasible in field applications (in the long term) still needs further exploration. Therefore, applications of sustainable managements and intelligent measurements are necessary in soil–plant systems.

This Special Issue invites original research, technology reports, modeling approaches and methods, and reviews on sustainable management and intelligence in soil–plant systems. The topics of interest include (but are not limited to):

Sustainable management (e.g., optimized irrigation and fertilizer practices, cropping systems, and agronomic strategies) on the improvement of soil quality, plant growth, productivity, and tolerance to drought;
Implications of intelligent methods (e.g., sensing techniques, multiple scales of phenotyping platforms) on soil and vegetation health monitoring;
Interactions between agricultural water/fertilizer management and the environment;
Interactions between soil and plant in contaminated soils;
New biomaterials for improving water use efficiency in soil–plant systems;
Applications of unsaturated soil concept in sustainable agriculture;
Development of IoT-based devices and APPs for smart agriculture.

Dr. Xuguang Xing
Dr. Ankit Garg
Dr. Long Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

agricultural production
crop modeling
deep learning
intelligent agriculture
plant abiotic stress
precision farming technology
soil–plant interaction
saline–alkali soil
tillage
water/fertilization management

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Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods—2nd Edition in Agronomy (1 article)

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Editorial

Jump to: Research, Review

3 pages, 183 KiB

Open AccessEditorial

Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods

by Boneng Chen, Ankit Garg, Xuguang Xing and Long Zhao

Agronomy 2023, 13(11), 2715; https://doi.org/10.3390/agronomy13112715 - 28 Oct 2023

Viewed by 860

Abstract

We are privileged to serve as Guest Editors for this Special Issue (SI), “Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods”, in the international journal Agronomy [...] Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

Research

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15 pages, 2881 KiB

Open AccessArticle

Temporal and Spatial Assessment of Soil Salinity Post-Flood Irrigation: A Guide to Optimal Cotton Sowing Timing

by Yujiang He, Xianwen Li and Menggui Jin

Agronomy 2023, 13(9), 2246; https://doi.org/10.3390/agronomy13092246 - 27 Aug 2023

Cited by 1 | Viewed by 822

Abstract

Flood irrigation is often applied in the arid regions of Northwest China to facilitate the leaching of salts accumulated in the soil during cotton growth in the previous season. This will, in turn, affect the temporal and spatial patterns of soil salinity, and thus cotton germination. To reveal the salinity of the two soil layers (0–20 cm and 20–60 cm), so as to determine the optimal cotton sowing timing, an electronic ground conductivity meter (EM38-MK2) was employed to measure the soil apparent electrical-conductivity (EC_a) on different days: 4 days prior to flood irrigation, and, respectively, 6, 10, 15, 20, and 45 days after flood irrigation. Moreover, geostatistical analysis and block kriging interpolation were employed to analyze the spatial-temporal variations of soil salinity introduced by flood irrigation. Our results indicate that: (1) soil salinity in the two layers on different days can be well inverted from binary first-order equations of EC_a at two coils (i.e., EC_a1.0 and EC_a0.5), demonstrating the feasibility of applying EM38-MK2 to estimate soil salinity in the field; and (2) soil salinity in the 0–20 cm layer significantly decreased during the first 15 days after flood irrigation with the greatest leaching rate of 88.37%, but tended to increase afterwards. However, the salinity in the 20–60 cm layer was persistently high before and after flood irrigation, with merely a brief decrease during the first 10 days after flood irrigation at the highest leaching rate of 40.74%. (3) The optimal semi-variance models illustrate that, after flood irrigation, the sill value (C₀ + C) in the 0–20 cm layer decreased sharply, but the 20–60 cm Range of the layer significantly increased, suggesting that flood irrigation not only reduces the spatial variability of surface soil salinity, but also enhances spatial dependence in the 20–60 cm layer. (4) The correlation of the soil salinity between the two soil layers was very poor before flood irrigation, but gradually enhanced during the first 15 days after flood irrigation. Overall, for the study year, the first 15 days after flood irrigation was an optimal timing for cotton sowing when the leaching effects during flood irrigation were most efficient, and overrode the effects of evaporation and microtopography. Although not directly applicable to other years or regions, the electromagnetic induction surveys and spatiotemporal analysis of soil salinity can provide a rapid and viable guide to help determine optimal cotton sowing timing. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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16 pages, 1934 KiB

Open AccessArticle

Influence of Interlayer Soil on the Water Infiltration Characteristics of Heavy Saline–Alkali Soil in Southern Xinjiang

by Hongbo Liu, Bin Wu, Jianghui Zhang, Yungang Bai, Xianwen Li and Bo Zhang

Agronomy 2023, 13(7), 1912; https://doi.org/10.3390/agronomy13071912 - 20 Jul 2023

Cited by 3 | Viewed by 972

Abstract

Interlayer soil is common in southern Xinjiang, because interlayer can reduce the infiltration rate of soil water. To simulate the interlayer soil in heavy saline–alkali cotton fields, this paper adopted a vertical one-dimensional infiltration test. T1 (315 mm), T2 (270 mm), and T3 (225 mm) and different interlayer positions (T5, 315 mm) and thicknesses of the interlayer (T6, 315 mm) with the same irrigation volume, as well as one perforation and sand filling treatment (T4, 315 mm), were set. The influence of different irrigation amounts, locations, and thicknesses of the interlayer and sand injection on water infiltration was analyzed. The analysis results showed that with the increase in irrigation amount, the water infiltration rate and the migration distance of the wet front increased, but did not penetrate to the bottom soil (90 cm). Under the same irrigation volume, the increase in interlayer thickness (T6) compared with the increase in interlayer position (T5), the change in soil moisture content in the upper and lower layers of the interlayer was greater, and the advance time of wetting front migration and cumulative infiltration were slightly higher. After tunneling and sand filling (T4), the infiltration rate of water was increased, the migration time of the wet front was reduced, and the profile water content of each soil layer was improved. The Kostiakov model could better simulate the water infiltration characteristics of interlayer soil with different profile configurations in heavily saline–alkali land. The results showed that in all of the treatments, only the wet front of the soil moisture reached 100 cm in the T4 treatment, and the maximum was only 87.8 cm in the other treatments, indicating that too little irrigation water or the upward movement and thickening of the interlayer were not conducive to water infiltration. For the interlayer soil area in the heavy saline–alkali land in southern Xinjiang, the appropriate irrigation water should be more than 315 mm. The treatment of drilling first and then filling sand can be used as a simple but effective measure to increase the water infiltration rate of the interlayer soil, and can thus be applied to the layered soil structure in the interlayer position of 60–80 cm. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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20 pages, 6526 KiB

Open AccessArticle

Design and Implementation of Evaluation Method for Spraying Coverage Region of Plant Protection UAV

by Kun Zhang, Long Zhao, Jingying Cui, Pengjun Mao, Bohan Yuan and Yuyang Liu

Agronomy 2023, 13(6), 1631; https://doi.org/10.3390/agronomy13061631 - 18 Jun 2023

Cited by 3 | Viewed by 1023

Abstract

Plant protection UAVs are becoming the preferred plant protection method for agricultural pest control. At present, the evaluation of droplet distribution in aerial spraying is collected and evaluated after the completion of prevention and control operations, and there is a lack of real-time evaluation methods. Based on the flight parameter during the UAV plant protection process, real-time estimation of droplet distribution is the key to solving this problem and further improving the effectiveness of aerial spraying. This study proposes a merging algorithm for arbitrary polygonal regions, meshing the boundaries of the region, divide the mesh segments based on the overlapping meshes between the two regions, and connect the valid mesh connection segments of the two regions according to certain rules to obtain the intersection, union, and residual operation results between the regions. Afterwards, software based on this algorithm was developed and applied to generate spraying coverage regions, leakage spray regions, and repeated spray regions. The experimental results on theoretical and irregular routes show that the algorithm can accurately generate droplet distribution regions. The error of the calculation results with a mesh scale of 0.05 m is within 7‰, and the operating speed is above 30 Hz, meeting the real-time requirements. The smaller the mesh scale is, the higher the accuracy of the calculation results is, but the slower the calculation speed. Therefore, in practical applications, it is necessary to choose an appropriate mesh scale based on hardware computing power and accuracy level requirements. This study solves the problem of cumulative calculation of droplet distribution during the operation of plant protection UAVs, providing a basis for objectively evaluating the operation quality of plant protection UAVs and optimizing the setting of operation parameters. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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10 pages, 2119 KiB

Open AccessArticle

Effect Application of Apple Pomace on Yield of Spring Wheat in Potting Experiment

by Marcin Różewicz, Marta Wyzińska and Jerzy Grabiński

Agronomy 2023, 13(6), 1526; https://doi.org/10.3390/agronomy13061526 - 31 May 2023

Cited by 1 | Viewed by 1337

Abstract

Apple pomace, as a by-product, is difficult to manage and is produced in significant quantities. This makes it necessary to manage the resulting biomass. It is important for the environment to use pomace in an ecological way. It can provide a source of organic matter and be composted, but it can also be added directly to the soil. The greatest impediments in the use of pomace are the constant process of its production and the fermentation processes taking place within it, which require storage of action and drying and transportation of the pomace. Using pomace immediately after its formation as an exogenous source of organic matter for soil is a possibility. This method of pomace management benefits society and the natural environment. Thus, a study was undertaken to determine the feasibility of applying apple pomace to soil in a model experiment. Tests were conducted on spring wheat of the Harenda cultivar in a greenhouse. Various amounts of apple pomace were added to the soil. Soil properties were studied, as well as photosynthetic parameters and crop yield structure. It was shown that it is possible to improve soil properties and plant yield by adding pomace to the soil, but only for a limited quantity of pomace. The highest maximum pomace that should be used, for spring wheat in field conditions, is a maximum of 2 t/ha⁻¹. At this dose of apple pomace, the characteristics of the wheat yielding structure were significantly improved, such as plant tillering, the number of ears and the weight of kernels per spike, and the weight of a thousand kernels. Since this was a model experiment, it should be treated as an introduction to research on the use of pomace, and further research on the possibility of using pomace in field conditions, including for other cereal species, should be continued. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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19 pages, 1370 KiB

Open AccessArticle

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

by Florin Sala and Mihai Valentin Herbei

Agronomy 2023, 13(6), 1500; https://doi.org/10.3390/agronomy13061500 - 30 May 2023

Cited by 3 | Viewed by 1773

Abstract

Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, “Alex” cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (N_fd, P_fd, K_fd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R² = 0.763, p = 0.013 for the model based on applied fertilizers (NPK_F); R² = 0.883, p < 0.01 for the model based on foliar diagnosis (NPK_fd); R² = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R² = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R² = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R² = 0.717, compared to R² = 0.763 for the F model in this study. The models obtained in this study, related to the “Alex” wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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24 pages, 8042 KiB

Open AccessArticle

A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

by Jian Gao, Wenzhi Zeng, Zhipeng Ren, Chang Ao, Guoqing Lei, Thomas Gaiser and Amit Kumar Srivastava

Agronomy 2023, 13(5), 1400; https://doi.org/10.3390/agronomy13051400 - 19 May 2023

Cited by 4 | Viewed by 2127

Abstract

Background: The application of base fertilizer is significant for reducing agricultural costs, non-point source pollution, and increasing crop production. However, the existing fertilization decision methods require many field observations and have high prices for popularization and application. Methods: This study proposes an innovative model integrating machine learning (ML) and swarm intelligence search algorithms to overcome the above issues. Based on historical data for maize, rice, and soybean crops, ML algorithms including random forest (RF), extreme random tree (ERT), and extreme gradient boosting (XGBoost) were evaluated for predicting crop yield. Coupled with the cuckoo search algorithm (CSA), the prime fertilization decision model (FDM) was established to discover the optimal fertilization strategy. Result: For all three crops, the yield simulation accuracy of the ERT model was the highest, with an R² and RRMSE of 0.749, 0.775, and 0.744, and 0.086, 0.051, and 0.078, respectively. Considering soil nutrient and fertilization characteristics as the determinants of yield and optimizing fertilization strategies, the proposed model can increase the average yield of maize, rice, and soybean in the study area by 23.9%, 13.3%, and 20.3%, respectively. Conclusions: The coupling model of ERT and the CSA constructed in this study can be used for the intelligent and rapid decision-making of the base fertilizer application for crops considered in the present study. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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14 pages, 3013 KiB

Open AccessArticle

Municipal Waste Degradation by Vermicomposting Using a Combination of Eisenia fetida and Lumbricus rubellus Species

by Hira Khalid, Amir Ikhlaq, Usman Pervaiz, Young-Min Wie, Eui-Jong Lee and Kang-Hoon Lee

Agronomy 2023, 13(5), 1370; https://doi.org/10.3390/agronomy13051370 - 12 May 2023

Cited by 1 | Viewed by 1654

Abstract

Earthworms have been commonly used for solid waste management by employing the process of vermicomposting. In this study, we used two different types of earthworm for vermicomposting and analyzed their efficacy for plant production in comparison to chemical fertilizer. The worms used for vermicomposting included Eisenia fetida (EF) and Lumbricus rubellus (LR), and we studied compost efficiency for the harvesting of spinach and turnips. The parameters we used to evaluate the performance of the produced compost on crops were variations in sprouting time, harvesting time, plant height, and plant yield. For the production of compost, the waste was collected and degraded in an environment where various parameters, including pH, moisture content, temperature, carbon, and nitrogen, were measured regularly throughout the experiment. The compost obtained from these three setups was used as a fertilizer to grow spinach and turnip. Compost efficiency was compared based on plant yield, plant height, sprouting, and harvesting time. In the case of turnip, the combination worm compost yielded 38% and 58% more than the compost obtained using EF and LR, respectively. For spinach, the EF–LR combined compost gave similar results, 17.4% and 37.9% more yield than the above two worms individually. The study’s results showed that the compost obtained by the combination of worms is more promising than the compost obtained from a single species and applied as fertilizer. Moreover, the comparative evaluation by statistical analysis confirmed that growing spinach by combined compost would be a better option compared to growing turnip, due to higher significant difference in outcome parameters. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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21 pages, 2491 KiB

Open AccessArticle

Effects of Long-Term Organic–Inorganic Nitrogen Application on Maize Yield and Nitrogen-Containing Gas Emission

by Hui Zhou, Yongqiang Wang, Jian Wang, Hu Liu, Hongfang Li and Jiawei Guo

Agronomy 2023, 13(3), 848; https://doi.org/10.3390/agronomy13030848 - 14 Mar 2023

Cited by 5 | Viewed by 1549

Abstract

A sustainable model of combined organic–inorganic fertilizer application for high maize yields and environmental health is important for food security. The short-term combined application of organic and inorganic fertilizers can improve crop yields; however, the effect of different proportions of organic and inorganic fertilizers on the maize yield and nitrogen gas emissions in a long time series has not been reported. In this study, field experiments and DeNitrification-DeComposition (DNDC) model simulations were used to study the long-term effects of substituting inorganic fertilizers with organic fertilizers on crop yields and nitrogen-containing gas emissions. Six treatments were included: no nitrogen (CK); urea (U1); and 25%, 50%, 75%, and 100% of the urea N substituted by organic fertilizers (U3O1, U1O1, U1O3, and O1, respectively). The DNDC model was calibrated using the field data from the U1 treatment from 2018 to 2020 and was validated for the other treatments. The results showed that this model could effectively simulate crop yields (e.g., nRMSE < 5%), soil NH₃ volatilization, and N₂O emissions (nRMSE < 25%). In addition, long-term (26 years) simulation studies found that the U1O1 treatment could considerably increase maize yields and ensure yield stability, which was 15.69–55.31% higher than that of the U1 treatment. The N₂O, NH₃, and NO emissions were in the descending order of U1 > U3O1 > O1 > U1O3 > U1O1, and the total nitrogen-containing gas emissions from the U1O1 treatment decreased by 53.72% compared with the U1 treatment (26 years). Overall, substituting 50% of inorganic nitrogen with organic nitrogen could maintain the high yield of maize and reduce emissions of nitrogen-containing gases, constituting a good mode for the combined application of organic–inorganic nitrogen in this area. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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17 pages, 3188 KiB

Open AccessArticle

How Do Microplastics Affect Physical Properties of Silt Loam Soil under Wetting–Drying Cycles?

by Xiaoyuan Jing, Liuchang Su, Yisen Wang, Miao Yu and Xuguang Xing

Agronomy 2023, 13(3), 844; https://doi.org/10.3390/agronomy13030844 - 14 Mar 2023

Cited by 3 | Viewed by 1723

Abstract

Soil physical properties are the main factors that influence soil fertility and directly affect the soil structure and water storage capacity. Microplastics (MPs), which have caused growing concern with respect to soil pollution, have readily been detected in cultivated soils. However, the current data regarding the effects of MPs on soil physical properties during wetting–drying cycles remain insufficient. Therefore, we aimed to explore the effects of different MP particle sizes (25, 150, 550, and 1000 μm) and concentrations (1, 3, and 5%, w/w) on soil physical properties under indoor wetting–drying cycle conditions. The addition of MPs was found to significantly reduce the saturated hydraulic conductivity and water holding capacity of soil, while impacting the bulk density, water content, and soil particle composition. The properties of soils treated with different MP particle sizes and concentrations exhibited significant differences, while the effects of wetting–drying cycles overshadowed those of MPs. Under the wetting–drying cycles, the saturated hydraulic conductivity and initial soil water content decreased significantly, the soil water holding capacity increased, and the soil bulk density showed a trend of increasing first and then decreasing. We attribute the change to a combination of the microplastics, soil particles, and frequent wetting–drying cycles. In this type of incubation, the constant change in the soil pore proportion results in a change in water and soil porosity, and finally alters the soil physical properties. These findings demonstrate that MP accumulation, together with dynamic environmental conditions, significantly impacts the physical properties of farm land soil. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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24 pages, 7906 KiB

Open AccessArticle

Estimating Soil Hydraulic Parameters during Ponding Infiltration Using a Hybrid Algorithm

by Yibo Li, Ye Liu and Xiaoyi Ma

Agronomy 2023, 13(3), 726; https://doi.org/10.3390/agronomy13030726 - 28 Feb 2023

Cited by 1 | Viewed by 1013

Abstract

Accurate inversion of soil hydraulic parameters based on the van Genuchten–Mualem model has received much attention in soil science research. Herein, a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm was used to invert the parameters θ_s, α, n, and K_s, with the objective functions of infiltration rate, cumulative infiltration, and soil water content. Then, numerical experiments were conducted on four typical soils at three initial water content levels (20, 40, and 60% effective saturation) to verify the accuracy of the inverse method. The results showed that the inversed soil water retention and conductivity curves were approximately the same as the real curves, with the root mean square errors of 0.00101–0.00192 cm³·cm⁻³, 0.00800–0.02519 cm³·cm⁻³, respectively, and both the Nash-Sutcliffe coefficients were approximately 1.0. Additionally, laboratory experiments were also performed to compare with the inversed parameters for verification, within small root mean squared errors and approximately 1.0 Nash–Sutcliffe coefficients. Furthermore, the method can also achieve acceptably accurate parameter inversion even with substantial measurement errors included in the cumulative infiltration, initial water content, and final water content. Thus, the method is effective and robust and found to be practical in field experiments. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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Review

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30 pages, 3997 KiB

Open AccessEditor’s ChoiceReview

Deciphering the Interactions in the Root–Soil Nexus Caused by Urease and Nitrification Inhibitors: A Review

by Sneha Gupta, Sibel Yildirim, Benjamin Andrikopoulos, Uta Wille and Ute Roessner

Agronomy 2023, 13(6), 1603; https://doi.org/10.3390/agronomy13061603 - 13 Jun 2023

Cited by 4 | Viewed by 2496

Abstract

Optimizing nitrogen (N) availability to plants is crucial for achieving maximum crop yield and quality. However, ensuring the appropriate supply of N to crops is challenging due to the various pathways through which N can be lost, such as ammonia (NH₃) volatilization, nitrous oxide emissions, denitrification, nitrate (NO₃⁻) leaching, and runoff. Additionally, N can become immobilized by soil minerals when ammonium (NH₄⁺) gets trapped in the interlayers of clay minerals. Although synchronizing N availability with plant uptake could potentially reduce N loss, this approach is hindered by the fact that N loss from crop fields is typically influenced by a combination of management practices (which can be controlled) and weather dynamics, particularly precipitation, temperature fluctuations, and wind (which are beyond our control). In recent years, the use of urease and nitrification inhibitors has emerged as a strategy to temporarily delay the microbiological transformations of N-based fertilizers, thereby synchronizing N availability with plant uptake and mitigating N loss. Urease inhibitors slow down the hydrolysis of urea to NH₄⁺ and reduce nitrogen loss through NH₃ volatilization. Nitrification inhibitors temporarily inhibit soil bacteria (Nitrosomonas spp.) that convert NH₄⁺ to nitrite (NO₂⁻), thereby slowing down the first and rate-determining step of the nitrification process and reducing nitrogen loss as NO₃⁻ or through denitrification. This review aims to provide a comprehensive understanding of urease and nitrification inhibitor technologies and their profound implications for plants and root nitrogen uptake. It underscores the critical need to develop design principles for inhibitors with enhanced efficiency, highlighting their potential to revolutionize agricultural practices. Furthermore, this review offers valuable insights into future directions for inhibitor usage and emphasizes the essential traits that superior inhibitors should possess, thereby paving the way for innovative advancements in optimizing nitrogen management and ensuring sustainable crop production. Full article

(This article belongs to the Special Issue Improving Functioning of Soil–Plant Systems Using the Application of Sustainable and Intelligent Methods)

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