Neutralization of soil acidity using CaCO3, increase in pH, precipitation of aluminum, and availability of phosphorus from a Purple Latosol.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53300",title:"Problems Faced While Simulating Nanofluids",doi:"10.5772/66495",slug:"problems-faced-while-simulating-nanofluids",body:'\nCouple of decades back, nanofluid research was mostly conducted using experimental techniques. With time, as the computational power acquired drastic developments, new algorithms were designed, and therefore, today, we have got sophisticated software and mathematical models to solve and simulate the nanofluid environment.
\nNanofluids comprises of two constitutes, i.e. Nano comes from nanoparticles and fluid comes from base fluid. The need of combining nanoparticles with fluid was necessary for enhancing the properties of the base fluid. Addition of nanoparticles to the base fluid helps in altering and optimizing properties such as physiochemical [1], thermo-physical [2], rheological [2–4], etc.; to give a new composite performance. The initial mixing of nanofluids can be dated back to the time of the US choi in 1995, he was the first one to form nanofluid at Argonne laboratories USA [5, 6]. He used the nanofluid for optimization of thermal conductivity. Since then there have been several experimental studies over the thermal conductivity analysis of different nanoparticles in various base fluids [5, 7–9].
\nBy looking at thermal conductivity improvement, other researchers came up with different ideas and formulations for utilization of this technique in various fields of science. Today, nanofluids are being used in biological, pharmaceuticals and medicine [10], engineering [7], lubrication industries [11, 12]. The major work on experimental side in all these industries has been carried out; however, these experiments of nanofluid require high skilled labour and expensive equipment. Furthermore, material purchase and characterization are costly. Due to this, researchers and industrialists working with nanofluids are trying to develop a model that can replicate mechanisms dealing with nanoparticle and fluid interactions. However, this subject is wide and requires huge expertise to deal with.
\nCurrently, as the computational power has enhanced to a level where people are finding it easy to simulate and replicate systems within their personal computers, it is now becoming quite manageable task to simulate nanofluids. But the task is not as simple as it seems, it requires a lot of understanding of physiochemical interactions with thermo-physical boundary conditions. There are many algorithms and mathematical models to be considered. As the number of these models and algorithms increases, higher the computational power is required for solving. Nevertheless, the endless applications and usage makes it convincible for an end-user to adopt this creativity, as it enables one to understand the process and makes it visually quantifiable.
\nBefore moving forward, it is necessary to understand some basic theory that is behind the dispersion of nanoparticles within a certain fluid.
\nDispersion of nanoparticles is a process in which they are dispersed in a medium like fluid. These fluids are of different grades such as biological, aerospace, automotive and buffering solutions. According to the kinetic theory of molecules, as the molecule interacts with other molecule, it starts to generate some heat due to kinetic molecular movement of the particle. This movement is accountable for the dispersion of nanoparticles in different fluids; thereby, this model causes anomalous increase in the heat transfer of the nanofluids. Furthermore, using this model, four major effects produced by nanoparticles dispersion can be explained i.e. (a) Brownian motion of nanoparticle, (b) liquid layering at liquid particle interface, (c) nature of heat transport between nanoparticles and (d) the clustering effect of nanoparticles in fluid. These factors are responsible for inducing random motion within particle and liquid layers, and this phenomenon is Brownian motion. During the interaction between nanoparticle and fluid, heat is evolved, causing nanoparticles to cluster and agglomerate. These mechanisms have already been replicated by various researchers for analysing properties such as; (a) rheological, (b) thermo-physical and (c) physiochemical as mentioned in Section 1.2.
There are various applications in the area of nanofluid simulation. Currently, nanofluid simulation is being applied for analysing the rheological properties of nanofluid environment, which is useful for biological, oil and gas, lubrication and chemical industries. Now, by the help of simulation, it is possible to test those undesirable conditions that could not be tested before, such as testing viscosity at low and very high temperatures. Properties of ideal nanofluid can be tested and their results can also be validated using autocorrelation functions for satisfaction.
\nThe use of molecular dynamics has enabled us to test and quantify thermo-physical quantities of nanofluid at obnoxious level. The chemical interactions that were complicated to understand from the real interface, now it has become straightforward to know how the atoms of fluid and nanoparticle interacts together, nevertheless, Brownian dynamics is more appreciably demonstrated and visualized. Having this all, analysing different properties of fluid and nanoparticle interaction, now it is easy to know other parameters such as specific heat [13], total energy, bond formation at molecular level, chemical interactions, etc. [14]. Furthermore, various effects that could not be judged by experimental testing can now easily be known such as the effect of liquid layering on thermal conductivity as investigated by Li et al. [15]. Particle effect on thermal conductivity analysis can now be determined as carried out by Lu and Fan [16]. Nevertheless, effect of surfactant addition in nanofluid system can also be tested using molecular dynamics, which can better tell about the chemical interaction and aggregation dynamics within this system as conveyed by Mingxiang and Lenore [17]. Rudyak also succeeded in showing that by changing nanoparticle size and shape effects the viscosity [18]. Therefore, by looking at the vast applications of nanofluid simulation, it is necessary to know some overview about how these simulations can easily be conducted.
Simulations are being preferred over experimental practices in the twenty-first century. As experiments require a lot of man power and material, which is costly and time-consuming, therefore, researchers are favouring simulations, as it saves material, money and time. With the advancement in computational technology, simulations are being approached to replicate the nanofluids. Simulations are not an old technique, and it has got a firm ground. Currently, the area of simulation to replicate the real phenomena of dispersion is through the intermediate stages. Before moving to simulations, it is important to understand dispersion and interaction mechanism of nanoparticles with fluids. For this, the major phenomena that is used for dispersion is Brownian motion, which is an important aspect that controls the random factor of nanoparticle dispersion.
Nowadays, the necessity of using simulation techniques is increasing due to its cost-effectiveness and time-saving capabilities. Simulations for nanofluids are mostly referred to as molecular dynamics simulation (MDS). However, before MDS, researchers adopted theoretical and numerical calculation method for computing thermo-physical quantities. Earlier theoretical formation, related to MDS research, has not established a strong hold position for replicating the mechanism of heat transfer, rheology and thermo-physics involved for nanofluid dispersion. This is because several researchers had modelled system using various assumptions rather using a definite formulation. This creates ambiguity in collecting results; however, they were well utilized for initial prediction of thermal transfer properties of nanofluid at the cost of wide inaccuracies. Experimental results that are representing actual system sometime are way off from the ideal method, in addition to this, researchers apply various differential equations for equating the system to realistic results as possible.
\nThese methods are single-phase and two-phase methods [19] of nanofluid heat convection. They are still being used for predicting several properties related to heat transfer, convection and conduction within nanofluid systems [19–21]. Now these two methods are being embedded in computation fluid dynamic and molecular dynamics for heat transfer analysis [21]. The single-phase method of heat convection in nanofluid is an old method and is good for initial prediction of the thermal properties of nanofluid; however, the second-phase method is costlier as it requires higher computing power. In addition to the second-phase method, it is quite versatile as its prediction is in higher accuracy to the experimental results. Numerical approach simulates the nanofluid system using classical thermodynamics principles, which is more close to the single-phase model. Different correlations are applied to estimate the imbalance between the heat propagation values from actual to the ideal system. Physical interaction kinetics involved in real nanofluid system are not mimicked. This is why the real prediction is hard to achieve by this approach; moreover, two-phase fluid heat transfer involves higher mathematical complexity, which requires high computational power for general analysis of nanofluid heat transfer, rheology and thermo-physical quantities.
\nIt was investigated by Sergis Antonis that due to not standardizing the procedure of nanofluid preparation diversifies accuracy of the experimental results obtained [2]. In this respect, MDS comes in to play, as it helps in simulating both nanoparticle and fluid particle system in one single domain, enabling us to mimic reaction kinetics of both materials in one single domain. However, these simulations require high computational power for simulating the system as it involves kinetic molecular movement of different atoms. Initially, MDS involved heat transfer within a nanofluid system in which it did not involve analysis with respect to the geometrical features or spherical with no surface texture. It used to be simple analysis in a uniform and homogeneous system. Earlier, properties of SiO2 nanoparticles were calculated using Stillinger-Weber [22] and later fluid particles were represented by L-J potential.
\nThere are two different dispersion prospects of MDS i.e. (1) non-equilibrium MDS (NEMD) and (2) equilibrium MDS (EMD). The macroscopic MDS mimics the molecular interactions between different molecules of various elements; in compound or ionic form. These different thermo-physical types of interactions of molecular dynamic quantities can be tailored and analysed by true boundary conditions. These boundary conditions are related to the physical settings, chemical interactions, charges, viscosity of the system and motion exhibition of particles. The interaction between the molecules is exhibited by Brownian motion as this mimics the random forces in the system. The system relies on different algorithms behind the scene to design a virtual nanoparticles dispersion in fluid. Furthermore, this is because the interaction kinetics of nanofluid system adhere with nanoparticle surface interacting with the surrounding fluid; this involves exchange of energy, surface tension between two, orientation of nanoparticle, surface energy, bonding configuration, nanoparticle dynamics and kinematics (including nanoparticle spin), liquid layering between nanoparticle and fluid molecule, and diffusion rate.
\nTo explain the trajectories and velocities of a fluidic system, it is necessary to adopt a hydrodynamic framework. Computer simulations for mimicking trajectory of hydrodynamic dispersion of a dispersed particle in a fluid system was used by Ermak [23]. Nevertheless, Ermak and McCammon [24] work was more focused on the hyrdrodynamically concentrated system. The hydrodynamical system exhibited that the inter-particle distance is much greater than the range of hydrodynamic interactions. However, by implementation of Brownian dynamics by Ermak gave highly concurrent results with the experimental values achieved. The hydrodynamics of the system display combinations of Coulomb interactions; i.e. long range interactions as well as the Vander Waal interactions; short range interactions. Furthermore, the dynamics of the system is more convincing after applying the Derjaguin, Landau, Verwey and Overbeek (DLVO) [25] theory/factor in the system to mimic the charges and to enhance the realistic intermolecular attractions and repulsions.
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Currently, there are different nanoparticles being considered for various applications. Therefore, for simulating nanofluids, modelling the nanoparticle is important, for that nanoparticle structure, shape and its properties should be known.
\nSubsequently, the mimicking of interaction potentials; i.e. using force fields such as embedded atom method (EAM), COMPASS, universal, etc; and the other forces between the atoms and molecules, the velocity verlet theorem is implemented. The velocity verlet theorem is a time-dependent movement of the atoms from one position to another using an algorithm for defining the movement, which is based on Brownian dynamics (BD). In addition to this, velocities or movements of atoms are controlled using thermal ensembles i.e. canonical (NVT), grand canonical (ΔPT), isobaric and isothermal (NPT) and micro canonical (NVE). These ensembles support in conducting thermal and physical perturbation to change the dynamical position of the atoms and molecules within a desired system. This causes the system to move to an un-equilibrium state. After starting and moving from an un-equilibrium state, the system is then equilibrated for convergence to equilibrium state. Finally, by this convergence, the system acquires stability of temperature and physical quantity fluctuations. However, this convergence is an iterative process for which time steps are varied to achieve the real convergence results [26, 27].
\nCurrently, there are various simulation of nanofluids, for example; CuO, TiO2 and CeO2 nanoparticle dispersion in water [3, 4]; furthermore, there are also studies of dispersing nanoparticles in hydrocarbons [28]. By having two different simulation strategies, a perspectives and robust methodology can be formulated. As these simulations are performed on two different types of fluids i.e. polar and non-polar, so a concurrent methodology for both fluids can be deduced. Furthermore, up to the date, investigators have carried out various researches on nanofluid MDS, in addition to this, last two decades of work has been cumulated in Figure 1. Following are the details of their work in the field of nanofluid simulations.
Timeline showing work carried out by different researchers since last two decades.
In 1998, Malevanets and Kapral [29] formulated a method for computing complex fluidic systems using H theorem, which helped in solving hydrodynamics equations and transport coefficients. Colloidal model and random stochastic movement algorithm was established using Brownian dynamics which was formulated by Lodge and Heyes [30].
\nFrancis W. Starr investigated effect of glass transition temperature on the bead spring polymer melts with a nanoscopic particle. He found that the surface interaction dominates due to nanoparticle diffusion within the melted polymeric system [31].
\nSimulation of chemical interactions was also carried out, and the bond length and structural orientation was noted for Silica nanoparticles in poly ethylene oxide (PEO) oligomer system. By this study, Barbier et al. concluded that the silica nanoparticles influence structural properties of PEO up to two to three layers [32].
\nMingxiang and Lenore worked on hydrocarbon surfactant in an aqueous environment with a nanoparticle diffused within this system. It was observed from interactions that the agglomeration created between water molecules and surfactant was independent of nanoparticle i.e. it does not matter whether it is present or not [17].
\nSarkar and Selvam designed a nanofluid system of Cu nanoparticle and Argon as basefluid, for this, he used EAM potential and Green Kubo technique to find the thermal conductivity of this system. He examined that the periodic oscillation existed due to the heat fluxes imposed by Leonard Jones (L-J) potential [9].
\nLi et al. later worked on similar system of Cu nanoparticle with Ar base fluid; however, they investigated Brownian dynamics induces a thin layer around a particle, giving a hydrodynamic effect to the particle dispersion [33].
\nLu and Fan investigated thermo-physical quantities of Alumina nanoparticles dispersed in water and concluded that the particle volume fraction and size effects the viscosity and thermal conductivity [16].
\nSankar et al. examined and formulated an algorithm for calculating metallic nanoparticle thermal conductivity in fluid. They articulated that the volume fraction of nanoparticles and temperature of the system effects the overall thermal conductivity [8].
\nMoreover, Cheung carried out research on L-J nanoparticles within solvent and quantified that the detachment energy decreases as the nanoparticle solvent attraction rises [1].
\nSun et al. devised a technique using EMD using Green Kubo method to find the effective thermal conductivity of the Cu nanoparticles in Ar liquid. It was found that there was a linear increase in the effective thermal conductivity of shearing nanofluid due to micro-convection [34].
\nRudyak and Krasnolutskii later on worked on Aluminium and Lithium nanoparticles with liquid Ar and suggested that the size and material of nanoparticle considerably effects the viscosity [18].
\nLin Yun Sheng et al. also detected increment in thermal conductivity by Cu nanoparticle dispersion in Ethylene glycol fluid. In this study, he used Green kubo formulation for finding thermal conductivity using NEMD [35].
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Furthermore, Mohebbi investigated a method to calculate thermal conductivity of nanoparticles in fluid using a non-periodic boundary conditions with EMD and NEMD [14].
\nKang H et al. carried out work on coupling factor between nanoparticle of Copper and Ar as base fluid, his investigations suggest that coupling factor is proportional to the volume concentration of particles, nevertheless, he also suggested the that there is no effect of temperature change from 90 to 200 K on coupling factor [36].
\x3c!--:The author name `Hongbo\' cited for the reference does not match with the reference list. Please check.
Rajabpour et al. investigated the specific heat capacity of Cu nanoparticles within water and he found that the specific heat capacity of this system decreases by increasing the volume fraction of particles in base fluid [13].
\nLoya et al. initiated work on CuO nanoparticles dispersion in water focusing on the change of viscosity due to temperature increase, he figured that temperature increment decreases the viscosity of nanofluid as also initially predicted using experimental testing [37].
\nIn addition to above, further rheological analysis of CuO nanoparticles in straight chain alkanes [28] and water [4] and CeO2 in water [3] was carried out by Loya et al. For conducting these simulations, molecular dynamics was used and studies provided highly accurate results of viscosity to experimental findings.
\nFinally, after knowing the perspective of nanofluid simulation, a simple and general way is deduced for researcher, industrialist and their co-worker in Section 2.3.
Several studies about simulation work were reported on the diffusion of polymeric, ionic and mineral nanoparticles [38–40]. An example of this is calcite nanoparticles. These have been simulated in water for salt molecular dynamics for thermal energy storage nanofluidic simulations [38]. Simulations such as these are mostly conceiving diffusions of the polymeric nanoparticles or di-block polymers represented by spheres. The major diffusion phenomena that have been implemented on the nanoparticle or the polymer dispersion is with the help of BD, targeting the random motion of the particles in a solvent or any solution system. Some further surveys show that one of the best simulations for the dispersion of the metal oxide nanoparticle in the water system was carried out using the DPD potential [41–43]. This potential has the power to disperse nanoparticles as well as replicating the phenomena of the BD [44]. DPD was first carried out on nano-water systems by Hooggerbrugge and Koelman [44, 45]. Moreover, the work was carried out by Español and Warren for implementing the DPD technique using statistical mechanics. DPD technique imparts stochastic phenomena on particle dynamics [46]. This is how BD was integrated into DPD technique. However, the random forces will only be in pairwise interaction since DPD at the same time imparts the hydrodynamic effect on the system. Many studies of DPD for complex fluidic systems [41–43] show that the dispersion of nanoparticles in water exhibits complex properties and to simulate this, initial selection of boundary conditions are important to replicate the real scenario. Thereby, the best way to simulate is to acquire the boundary conditions of the existing experimental system and then use a molecular dynamic simulator to further implement it [47]. The considerations of boundary conditions are particle sizes, force field for particle-to-particle interactions, solvent in which the particles will be diffused, and physiochemical nature of the system [48, 49]. Within the simulation system, force field plays an important role since it provides charges on atoms for interaction. The force field is a mathematical parameter that governs the energies and potentials between interactive atoms. The physiochemical settings of the system refer to the thermal, chemical and physical properties of the system such as initial temperature settings, charges and dynamics. Finally, the temperature is controlled using different ensembles.
The nanofluid interactions are carried out at molecular level. Therefore, by keeping this in mind to conduct nanofluid simulations, it is necessary to have a simulation technique which allows us to do simulation at molecular level. Hence, the technique use for this is molecular dynamics and package that is focused through this chapter is Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). Furthermore, how to approach this is mentioned in the next section of this chapter i.e. Section 3.1.1.
\nThe simulation of nanoparticle dispersion is related to the MDS. For this, the software or the package that needs to be selected was based on the criteria of the conditions that were needed to be simulated, and the flexibility was a major concern for the applicability of different systems. The LAMMPS can be a best molecular dynamics package for simulating the nanofluidic system. This is the code generated by the Sandia Laboratories by Plimpton [50]. This molecular dynamics software has high viability over other available software like Montecarlo and Gromacs.
\nAfter selection of the MD package, to simulate a desired system with realistic features, it is highly vital to know and understand initial boundary conditions. These initial conditions for a dispersion of nanoparticles are related to charges within the system for interaction, molecular bonding, forces of attraction i.e. Vander Waal or electrostatic coulombs interactions, forcefields, pair potentials (i.e. molecular mechanics constants) and molecular weight. To perform MD simulation, initial boundary conditions are major and fundamental parameters to devise actual dynamics that exist in a real system. After setting the initial parameters, velocity of the system is equilibrated and ensembles are applied to mimic the real thermo-physical conditions.
\nIn addition to above, after setting all the boundary conditions related to chemical and thermo-physical parameters, the system is then equilibrated for certain time steps. Simulations are processed until converging results are obtained as that of the actual system. Over here, “time step” is the major dependent factor. This accounts for equilibrating the kinetics of the system that takes place; i.e. movement of system from an un-equilibrated state to equilibrium conditions. The above explained method has been compressed and illustrated using a flowchart for better understanding as shown in Figure 2.
Flow chart of molecular dynamics simulation [26].
After suggesting how to approach and initiate your work for simulation of nanofluids, it is also important to know the briefed-out details about the steps like force field, pair potentials, ensembles, etc.
\nAfter setting up the atoms in a coordinate system using a molecular modelling software, then force field is applied on the system (i.e. Universal, COMPASS, OPLS, etc.) by this atomic charges and bond configurations are setup. These force fields are interlinked with pair potentials (such as DPD, BD, Smoothed Particle Hydrodynamic, LJ, etc.), they are parameters which are used to describe vibrational and oscillation settings between two different atoms. Finally, ensembles are applied on the molecular dynamic system for equilibrating the actual thermal settings for example NVT, NPT, NPH, etc.
As of now, it is known from the previous sections that to simulate and perform MDS it is necessary to know techniques and tools that can be beneficial for use and executing the work. Today, there are several tools and ways to perform this; however, still researchers are unsure about “what are the clear steps for conducting nanofluid simulations using molecular dynamics?” Therefore, through this section, a brief and concise way is illustrated and conveyed for better and easier understanding for people working under the horizon of nanofluid simulations. These steps are as follow:
\nFirstly, for creating nanofluid simulation system, it is required to setup a nanoparticle and fluid, then combine them together, for which material studio is the best software for designing a nanoparticle. Now, the nanoparticles can be inserted and replicated in a box containing fluid particles, however, this may be tedious for bigger systems. Therefore, it is suggested to use Packmol after creating the Protein Data Bank (PDB) file from material studio and then create an input script for Packmol to replicate the system with as many particles and fluid molecules as per required. This software automatically packs up the overall molecular arrangement with in a confined imaginary box.
As the nanofluid system is set up, now an input data file is required for LAMMPS software, this can be generated by using the PDB file and converting it to required .CAR and .COR format using Material studio. Before conversion do not forget to implement charges on the atoms of the nanoparticle and fluid molecules for this Discover module of the Material studio software can be used. After conversion to .CAR and .COR, use msi2lmp package provided with LAMMPS for converting the file to a LAMMPS readable input.
Once the LAMMPS readable input file is generated now use “read data” command for LAMMPS to read this file during the simulation execution.
Finally, the data quantification, visualizing the effects and properties that can be analysed have been jotted below in different sections.
Now, the data obtained by using different compute commands can be quantified on MATLAB or Excel. MATLAB initially requires more time for developing its script for computing the mathematical problem or graphs. However, on a long run, it does save time. Whereas, excel is easy going but requires more time for plotting graph each time you feed the new data.
\nMATLAB scripting helps in formulating the work in a precise manner, and digitalise the work with high quality publishing of the data for journal publications. However, MATLAB requires good command over the MATLAB scripting and functions. By using MATLAB, it is easy to apply discrete as well as continuous algorithms and equations for refining and optimization of results. Furthermore, it helps in applying the regression on the noisy data for refinement.
\nIn Excel, similar stuff is possible as in MATLAB, but in excel, it is quite complicated as you need to apply macros. These days the computation of MATLAB can be computed in parallel mode; again for excel, it is quite difficult. However, for graphical representation of data, excel is quite versatile.
\nVice versa both tools have their own benefits over each other; it depends totally on a user-friendliness with certain software. In addition to excel, to compute or establish complex calculations, it will be required to interlink its macros with visual basic scripting, which is under a developer’s tool library, mostly hidden from newbies.
After the successful execution of simulation, you will get dump files from LAMMPS, here a software that can read LAMMPS trajectories can be used for reading the file and visualizing it. For which Visual Molecular Dynamic (VMD) can be used. However, OVITO is also a good software for visualizing your trajectories.
\nThe results generated by OVITO are represented as small spheres merged together to form a particular system representation, as shown in Figure 3, i.e. of a CuO-water nanofluid system.
Representation of OVITO output of molecular dynamics of CuO nanoparticles in water system [4].
In the similar way for showing how the VMD gives visual output is shown in Figure 4. It is similar to that of OVITO, however, VMD has capability of representing the trajectories in the form of molecular structure. This gives an extra possibility for researchers working in the area of Biochemistry, pharmacy, drug delivery and biomedical to represent and observe the chemical kinetics in real-time, i.e. how one atom reacts and interacts with another atom within a confined system.
Visual output showing two CuO nanoparticles in a water-based nanofluid.
Some properties and parameters can directly be analysed using VMD using trajectories dump files. VMD has option for analysing the radial distribution function (RDF) and mean square displacement (MSD), they indicate about the agglomeration and dispersion rate, respectively.
\nWhen nanofluids are concerned the major parameters or properties researcher are interested to investigate are viscosity, thermal conductivity, specific heat capacity, thermal diffusivity, diffusion coefficient, total energy, heat loss, etc. To find these properties LAMMPS provide versatile options to compute what you require, using different algorithms or previously established techniques. Currently, main concerned variables out of above mentioned ones are viscosity, diffusion coefficient and thermal conductivity. Therefore, in the next section, we will discuss about how to validate and quantify your results obtained from the simulation.
To validate the three major properties mentioned in Section 3.1.5, it is required to know initial experimental results, however, sometime it is hard to obtain those results as some simulation condition cannot be tested, either due to lack of experimental device or it is not possible to meet the boundary conditions as setup over the simulation platform.
\nNow, in this case, the best way is to analyse using autocorrelation function; which is a time series modelling of a function of a variable dependent on time fluctuation. Let us take the case of viscosity, as it is related with shearing stress, there are shear forces acting between the layers of molecular interaction causing pressure function to be induced. This pressure function is dependent on stress due to shearing force. If this stress is analysed using the function of time, this becomes stress tensor. This stress tensor is used for analysing stresses exiting between the molecular layers. Therefore, this is known as stress autocorrelation function (SACF). The SACF accounts for the stresses imparted on the system due to the diffusion of molecules and intermolecular kinetics; i.e. molecular stresses caused by attraction and repulsion of molecules. During the intermolecular kinetics drag is created between the molecular layers, this drag is due to the effect of shearing forces. Ultimately as the system is equilibrated, it shows unstable response of the SACF, however, as it approaches stability the SACF starts to converge to a monotonic level, which satisfies that the viscosity analysed is acceptable.
\nIn the similar manner, thermal conductivity is quantified, but here instead of stress and shear forces, heat is considered. Therefore, this is known as heat autocorrelation function (HACF), which quantifies or validates the thermal conductivity obtained is satisfactory.
\nIn addition to HACF and SACF for thermal conductivity and viscosity, respectively, for diffusion coefficient, velocity autocorrelation function is used for its quantification. As diffusion coefficient is measured by taking the slope of the MSD. So to quantify and validate it, displacement with respect to time i.e. velocity can be used.
\nThe accuracy of results equilibrated for measuring the viscosity and thermal conductivity of a system can be justified in a better way with the estimation of heat autocorrelation function and stress autocorrelation function as show in Figure 5. The graphical result in Figure 5 explains the process of the integration of non-equilibrated system to equilibration.
Autocorrelation output gained by running a molecular dynamics simulations [26].
At step (a), the system starts with a thermodynamic equilibrium, but the system is not at equilibrium state. At step (b), the thermodynamic conditions are changed due to implementation of thermal ensemble so the system tends to go towards equilibrium. At step (c), the non-equilibrium system moves to equilibrated level of convergence at this level the system satisfies the convergences. This process is followed during the equilibration of the thermo-physical quantities, the convergence time steps depend on the volume and quantity of the atoms in that system. For the larger system, large amount of computational power and time step will be required for convergence.
So far the topic has been conveying the techniques, approach and method for carrying out nanofluid simulations. Moreover, there has been no data available for the expertise to know what are the problems faced when these simulations are conducted, number of questions can arise, for example, (1) Till what level, computational power can support our simulations? (2) Is there any other way out rather than this? (3) How larger systems can be simulated? etc.
\nTherefore, to answer these questions, it is necessary to understand the material and knowledge given before, however, as the number of atoms are increased within a nanofluid system the molecular dynamics demonstrates sluggish performance due to less computational capabilities i.e. either central processing unit (CPU) power or graphic processing unit (GPU). Furthermore, it is not just simulation that need to be carried out but for the data quantification, the data that are gathered requires huge memory for storage. Thereby, requiring the random access memory (RAM) and hard disk drive (HDD) to be large enough to store the required data easily [51].
\nAfter hardware issues, the second set of problems faced by nanofluid simulation is the use of multiple software for designing, modelling, processing and visualization, which needs a lot of understanding of computer for a new geek. Furthermore, if this all is combined in one package, this can marvellously save time and money for purchasing different software for data acquisition. It is slightly known at the moment that there are few software in market for helping in simulating nanofluid; however, academia is not yet aware of it due to less versatility such as Medea and Scienomics MAPS.
\nOne of the major problem is that, people of twenty-first century like working using graphical user interface (GUI), as it is easy and you can do everything by just clicks rather than using complicated commands, however, most of the molecular dynamics package are used on Linux operating system, moreover, commands are used for computing and feeding the data for computation.
\nIn addition to high computing power, it should be known that before attempting to simulate large scale molecular dynamics (i.e. with more than 0.1 million atoms), it is required to have parallel processing enabled on the PC. For that high end, CPU or GPU is required with multi cores for processing the data in parallel mode. However, this processing has some drawbacks that are loop holes for simulations, one such kind is that sometimes the algorithm is not designed in a way to parallel the process efficiently, which in turn gives ambiguous simulation output and convergence. For avoiding this, it is necessary for the user to know the correct working of the algorithm. Moreover, the field programmable gate array (FPGA) is good outbreak technology that is being implemented for paralleling the process [52, 53], nevertheless, again this technology requires new stuff and bits coding to be learned before operating or using this module for rapidly solving the simulation.
The chapter has brought about marvellous information and the literature for new geeks for conducting a nanofluid simulation. However, this chapter acts as a guide for a newbie for initialising the nanofluid simulation.
:References and were same, so the duplicate reference has been deleted and references are renumbered and cited accordingly. Please check if this is okay.
:Kindly check and update the reference [27] if possible.
Sugarcane is a crop adapted to tropical and subtropical climates, developing well between 37° N in southern Spain and 31° S in the Republic of South Africa. It is planted at altitudes ranging from sea level up to 1.00 m. In addition to the production of sugar and alcohol, sugarcane has been widely used by small and medium-sized rural producers for the production of cachaça, rapadura (raw brown sugar) and brown sugar, as well as for the feeding of ruminants and pigs, especially during times of high purchase price of corn or of low sale value of this monogastric. In order to increase the productivity of inputs, land and agriculture, agricultural techniques have been adopted, among which we may mention the improvement of soil physical and chemical properties by the application of lime and gypsum, chemical fertilization, green fertilization, and use of organic compounds. The choice of sugarcane varieties with a greater productive potential is another technology adopted by producers. For this, it is recommended to consult local or regional research agencies, as well as sugar mills and distilleries, to seek information on the adaptation and productivity of sugarcane varieties in different environments and different cultural managements [1].
\nThe average yields of sugarcane, including dry leaves and buds, oscillated around 100 tons of natural matter per hectare. However, by planting improved varieties and correcting and maintaining soil fertility by applying lime, gypsum and fertilization, it is possible to reach productivities of more than 150 tons of natural material per hectare. Complementary irrigation, especially that performed after sugarcane cutting, has resulted in high productivities and greater longevity of sugarcane plantations, as verified by authors in studies conducted in Paracatu, northwest of Minas Gerais, where they obtained an average productivity in two cuts of over 200 tons of industrializable culms per hectare per year [1].
\nIn order for sugarcane to have high stalk yields in the plant cane cycle and small decreases in ratoon yields, it is necessary to implement measures to maintain or increase soil fertility. Based on that, the present chapter aims to discuss the main technologies, related to soil fertility and mineral nutrition of plants, used for sugarcane production.
\nResearch has shown that there is a difference among sugarcane varieties in terms of efficiency in the absorption and use of nutrients. There are materials presenting a reasonable production even under conditions of low availability of such nutrients in the soil solution, while other varieties, at times more productive, are consequently more demanding. In the analysis of nutritional efficiency of a variety of sugarcane, its capacity to absorb and use nutrients for the production of dry biomass, protein and sucrose is quantified. The variety that, in the same soil and climatic conditions, accumulates more nutrients is considered more efficient in the absorption process, and the variety that produces a greater mass of sucrose or biomass in relation to mass of an absorbed nutrient is the most efficient in the use of such element [1]. It is desirable that the variety be efficient in both processes, but this is not always achieved.
\nCurrently, there are several sugarcane cultivars with good agronomic, industrial and zootechnical characteristics, such as adaptation to different edaphoclimatic environments, erect growth and resistance to falling, which facilitates harvesting, high yield of culms and sucrose, vigor of sprouts, tolerance to major pests and diseases, and a good dry matter digestibility. It is recommended to plant more than one variety of sugarcane so that, in case of an eventual break of disease resistance or a sudden problem with the cultivar, production will not be significantly compromised. When working with several varieties, varietal management should be adopted to use the good characteristics of each variety to the maximum. Having defined the varieties to be planted, it is necessary to make sure of the quality of seedlings. They should preferably be chosen from nurseries with a good sanity, ages varying between 9 and 12 months, and first, or at most, second cutting.
\nSugarcane, because it produces large amounts of mass, consequently extracts and accumulates a great quantity of nutrients from the soil. In studies conducted in Brazil, Australia, India, and Florida, it was found that for a production of 120 tons of natural matter per hectare, corresponding to about 100 tons of industrializable culms, the accumulation of nutrients in plant shoots must be 150, 40, 180, 90, 50, and 40 kg of N, P, K, Ca, Mg, and sulfur, respectively. In the case of the micronutrients iron, manganese, zinc, copper, and boron, the accumulations in shoot biomass, also for a production of 120 t, are around 8.0, 3.0, 0.6, 0.4, and 0.3, respectively [2, 3, 4]. Figure 1 shows the accumulation rate of macronutrients in the shoot biomass of RB867515 planted in February and harvested in July of the following year (“year and a half sugarcane”).
\nRate of nutrient accumulation in the shoot biomass of RB67515 planted in February and harvested in July of the following year (“year and a half sugarcane”).
Due to the high removal of nutrients by the sugarcane harvest, the nutrient supply capacity of the soil must be known to complement chemical and organic fertilization if necessary and, if there is presence of elements at toxic levels, to reduce its concentration by applying lime and gypsum. Normally, nutrient availability and presence of elements at toxic levels in the soil are evaluated by chemical soil analysis. The history of the area, especially fertilizations carried out, and whether or not there were symptoms of deficiency or of toxicity in previous cultures are also of great value [1, 2].
\nUsually, soil samples are collected from the layers 0–20 and 20–40 cm. The results of the analysis of the layer 0–20 cm will be used to calculate fertilization and liming, and the results of the layer 20–40 cm may be used for calculations of gypsum needed. In the traditional soil sampling system, the area is divided into homogeneous units, taking into account, among others, the history of the area, soil types (color, texture and depth), location and topography (lowlands, slope and plateau), vegetation cover, and previous fertilizations. The most commonly used instruments for collecting soil samples are augers and cutting blades, also known as straight blades. The use of augers in replacement for straight blades has the advantage of a greater speed in collecting simple samples, in handling and transporting a small soil volume in field before homogenization of simple samples, and in collecting composite samples. On the other hand, a low volume of collected soil causes variability of soil fertility indexes to increase, making it necessary to collect a high number of simple samples to form a representative composite sample. Even so, the laboriousness of soil sampling using augers is less than when using straight blades. At first, the use of instruments that collect a small soil volume, such as augers, would not be recommended for areas of minimal or no-tillage, where fertilization is performed in planting lines, preferring in such cases straight blades [1]. Regardless of the material used for sampling, care should be taken to always remove the same soil volume from each single sample.
\nIn large areas, grid soil sampling has been used. This technique consists in the collection of georeferenced soil samples. Due to georeferencing, it is possible to measure the variability of soil nutrient contents and to apply acid and fertilizer correctives at variable levels. In the traditional collection system, to obtain a composite sample, one must collect between 10 and 30 simple samples, numbers that depend on the size of the area and its homogeneity. On average, five simple samples per hectare are collected. After air-drying the composite sample, approximately 500 g of soil is collected to be packed in a properly identified container and sent to a chemical analysis laboratory.
\nIn Brazil, potassium, calcium, magnesium, sodium, and aluminum are analyzed as for exchangeable contents, and even though there is a great variation in the chemical extractors used by different laboratories, the accuracy of such analyses is high. Phosphorus, however, presents a greater reactivity with the soil, and its dynamics is also more complex. Thus, there are questions about the results of analyses performed in laboratories using different methods and extractors. However, analyses carried out by authors on soils from sugarcane regions in the state of Minas Gerais, Brazil, not fertilized with natural phosphate, indicated that there was no significant difference between available phosphorus levels extracted using Mehlich in relation to levels obtained using ion exchange resin. Sulfur and micronutrient contents varied greatly in relation to method and extractor used in soil chemical analysis, and there is still a great influence of collection time, soil moisture, and sample preparation [5]. Thus, the history of the area is of great value, especially regarding micronutrients, because if there is a record of deficiency in previous crops, it becomes necessary to include such deficient elements in fertilization.
\nMost soils cultivated with sugarcane in the world are acidic, presenting a low saturation by basic cations such as calcium, magnesium, and potassium. Deficiency of basic cations, associated with high levels of aluminum, iron, and manganese, is detrimental to the growth of the root system and, consequently, of the sugarcane plant as a whole. Al(OH)2+ and Al3+ are phytotoxic forms of aluminum that affect cell division, inhibit root growth, cause phosphorus precipitation both in the soil and inside roots, decrease the absorption of water and nutrients, and affect photosynthesis and, consequently, crop productivity. After applying limestone, there is an increase in the soil pH, and a neutralization of soil acidity precipitates aluminum and makes phosphorus available. Studies conducted by [6] on Purple Latosol showed that a pH increase from 4.0 to 5.0 precipitates aluminum totally and raises the phosphorus content from 4.8 to 24.2 mg/dm3 (Table 1).
\npH CaCl2 | \nCa | \nMg | \nK | \nAl+3 | \n(H + Al) | \nP | \n
---|---|---|---|---|---|---|
\n | (cmolc dm−3) | \n(mg dm−3) | \n||||
4.0 | \n1.80 | \n0.66 | \n0.37 | \n1.60 | \n12.56 | \n4.8 | \n
4.5 | \n4.40 | \n0.68 | \n0.38 | \n1.00 | \n10.00 | \n5.5 | \n
5.0 | \n7.6 | \n0.70 | \n0.35 | \n0.00 | \n6.73 | \n24.2 | \n
6.0 | \n10.60 | \n0.70 | \n0.36 | \n0.00 | \n3.66 | \n16.0 | \n
7.0 | \n15.00 | \n0.66 | \n0.36 | \n0.00 | \n0.20 | \n8.0 | \n
Neutralization of soil acidity using CaCO3, increase in pH, precipitation of aluminum, and availability of phosphorus from a Purple Latosol.
Source: adapted from [6].
The use of nitrogen fertilizers, mainly ammoniacal, and the removal of basic cations by harvesting may also contribute to soil acidity, which is why it has been common practice in sugarcane crops to correct soil acidity. Acidification caused by an ammoniacal fertilizer, ammonium sulfate, (NH4)2SO4, is exemplified below:
\nthen 2NH4+ originating from the dissociation of (NH4)2SO4 is oxidized by Nitrosomonas and Nitrobacter, producing 2NO3−. Thus, the acid reaction of ammonium sulfate can be described as:
\nSince 100 g of CaCO3 neutralizes 2.0 moles of 2H+, to neutralize 4H+, 200 g of CaCO3 is required. Several materials can be used as soil acidity correctors. The most used are calcitic limestones, magnesium and dolomitic limestones, and calcium and magnesium silicates, called steel plant slags. In these slags, the magnesium oxide content oscillates around 8%, while calcitic limestones have MgO contents lower than 5%, magnesium levels between 6 and 12%, and dolomitic levels above 12%. The efficiency of these products in the correction of soil acidity depends, among other factors, on their particle size, a uniform distribution in the field, and soil water availability. In relation to the corrective dose, there are some methods to estimate the quantity of product to be applied. Such methods are based on the particle size and neutralizing power of the corrective, as well as soil chemical characteristics, mainly calcium, magnesium, potassium, aluminum, and hydrogen contents.
\nIn the majority of Brazilian states, the corrective dose to be applied is estimated by neutralization of exchangeable acidity and increase in calcium and magnesium contents [7], or base saturation [8]. For sugarcane, it has been recommended to increase base saturation (V) to 60%. According to [8], the amount of limestone (QC) to be used, when adopting the base saturation criterion, is calculated by the following expression:
\nwhere V is the current base saturation of the soil, T is the cation exchange capacity at pH 7.0, and PRNT is the relative total neutralizing power of the corrective used.
\nStudies conducted by [9] on soils cultivated with sugarcane in the Minas Gerais state showed a need to use twice as many corrective levels as calculated using both methods [7, 8] to neutralize exchangeable aluminum or increase base saturation to 60% (Figure 2). Results similar to those described by [9] were obtained by [10, 11, 12] by comparing analytical methods to assess the need for limestone in the states of Santa Catarina, Paraná, and Mato Grosso. The authors also verified that base saturation underestimated at a high degree the need for limestone by the soils studied, especially the most buffered ones. Base saturation values lower than those predicted analytically were also found by [13] in a medium texture, alkaline Latosol cultivated with sugarcane. Ref. [12] in Campo Novo do Parecis and Nova Mutum (MT) verified that the increase in limestone doses estimated by base saturation ranged from 46 to 92%. Considering the observations of [10, 11, 12, 13], the authors recommended that, for areas with base saturation values below 30% or more clayey soils, the amount of limestone to be applied is 1.5–2.0 times as that calculated by Eq. (3) [8].
\nAluminum saturation (m%) at 40, 80, and 145 days after the beginning of incubation (DAI) of soil samples with dolomitic limestone and calcium silicate using one or two doses of corrective analytically predicted by base saturation.
In large sugarcane crops, many types of limestone distributors have been used, but, for small producers, the application is manual for most of the time. One method that authors have recommended for small producers is to demarcate a square or a rectangle with the limestone itself and, in this area, apply a corrective volume corresponding to the recommended dose. For example, supposing that the recommended dose was 4000 kg and the density of this correction is 1.25 kg/L, then 3200 L of corrective should be applied per hectare, or 0.32 L of the corrective/m2. One of the options for the producer to manually distribute limestone would be to demarcate 50 m2 areas with the limestone itself and apply 12.8 L of limestone. In Figure 3, a small sugarcane producer is applying limestone using this method to demarcate an area. Two bamboo sticks, spaced 10 m apart, can be seen at the bottom, with a plastic tape tied at the edge to serve as a marking for the demarcation of lines.
\nEquipment for the distribution of limestone in large sugarcane plantations, and a small rural producer applying limestone to previously demarcated areas.
There is a generalized conceptualization that the best Ca+2:Mg+2 ratio in the soil is 4:1. Therefore, the type of limestone (calcitic, magnesian, or dolomitic) to be used should be based on this ratio. On the other hand, some authors recommend exchangeable cation saturation in relation to the effective cation exchange capacity of the soil (t) at 80% of calcium, 13% of magnesium, and 6% of potassium, providing Ca:Mg, Ca:K, and Mg:K ratios of 6.15:1, 13.3:1, and 2.2:1, respectively. However, several studies have shown that the concentrations of Ca and Mg in the solution are more important than the relation between these cations [14]. In the case of corn, studies conducted by [14] indicated that variations in the soil Ca:Mg ratio from 1:1 to 12:1 in soils with exchangeable Ca and Mg contents above 2.32 and 0.40 cmolc dm−3, respectively, did not affect yield and production of corn dry matter.
\nThe sugarcane plantation areas and sugarcane planting using minimum and no-tillage systems have increased, following the tendency of corn and soybeans. In these systems, limestone is not incorporated as in the conventional tillage. However, the mineralization of crop remains and sugarcane straw, similar to what occurs in no-tillage areas with annual crops, releases organic anions that complex with Ca, Mg, K, and Al, forming electrically neutral molecules that percolate in the soil. In addition, such organic anions neutralize part of the soil acidity. Therefore, in such areas, liming should be performed only when base saturation at the 0–20 cm layer is lower than 40%.
\nIn a study conducted by [3] using lysimeters, it was verified that the sum of cation charges (K, Ca, Mg, and Na) was always greater than the sum of anion charges (nitrate, sulfate, and chloride) for the whole experimental period. Sulfate was the mineral anion with the highest concentration in the solution percolated in the soil, followed by chloride and nitrate. Initially, organic anions represented only 40% of the total negative charge, but there was a gradual and constant increase of these anions in the ionic balance of the percolated solution and, at the end of the experimental period, their share of the solution’s electroneutrality increased to 70%. Such results confirm, as in other studies, that organic anions originating from the mineralization of sugarcane remains or released by sugarcane roots must be involved in the nutrient leaching process by organometallic complexation with Ca, Mg, K, Al, and Na, which are present in the soil solution.
\nAgricultural gypsum, 10CaSO4.2H2O, a by-product of the fertilizer industry, originates from the reaction between sulfuric acid and phosphate rocks used to produce phosphoric acid. Gypsum applied to soil does not neutralize soil acidity but decreases aluminum saturation and increases base saturation of the subsurface, providing conditions for a further development and deepening of the sugarcane root system. It is recommended to apply gypsum when CaC2+ contents are lower than 0.4 cmolc dm−3 and/or aluminum saturation is greater than 20% at the 20–40 cm layer. The application of gypsum will lead to the improvement of the root environment at layers below arable ones, an effect that lasts for several years. For this reason, the annual reapplication of gypsum is not necessary. In areas with sugarcane straw or organic residues on the soil, and if the contents of Ca2+ are not very low and/or aluminum saturation is not very high, the response to gypsum may be lower.
\nThe doses of gypsum to be applied may be based on the need for liming, or on soil texture. The amount of gypsum to be applied varied between 25 and 30% for the need for liming, multiplied by a depth correction factor (profile to be corrected/20). For example, the amount of limestone to be applied was 3.0 t ha−1, and improvement of the root environment at the 20–60 cm layer is desired. Then, the amount of gypsum will be equal to 1.5 t ha−1[(3.0 x 0.25) x (60–20)/20]. When the doses of gypsum to be applied are based on soil texture, the following recommendation can be used [8]: dose to be applied (kg ha−1) = clay (g kg−1) x 6.0.
\nGypsum is applied in total area and may or may not be incorporated into the soil. When it is not possible to use it, mainly because of difficulty in acquiring it in small quantities, a fact that usually happens with micro and small farmers, one should choose to apply simple superphosphate as a source of phosphorus because this fertilizer contains calcium sulfate. In a study conducted by [15], limestone and gypsum rates were studied in a sugarcane crop cultivated in medium texture soils with a low cation exchange capacity. A relation between calcium levels in the soil and growth of the root system was also observed. Twenty-seven months after the beginning of the study, in a treatment with the application of 2.8 t of gypsum per hectare, the highest yield of biomass and industrializable shoots occurred. By soil analysis, a relation between exchangeable calcium and sugarcane root system was found: at 150 cm depth, Ca2+ was 0.60 cmolc/dm3 and the root mass was 1.1 g/dm3. Several authors have reported that under conditions of low availability of calcium in the soil, sugarcane roots concentrated at the layer 0–30 cm. However, in this study, 50% of the root system mass was in the layer 51–150 cm (Table 2).
\nLayer (cm) | \nExchangeable calcium (cmolc dm−3) | \nRoot mass (g dm−3) | \n% of root system | \n
---|---|---|---|
0–25 | \n2.10 | \n4.4 | \n29.93 | \n
26–50 | \n1.37 | \n3.0 | \n20.41 | \n
51–75 | \n0.90 | \n2.4 | \n16.33 | \n
76–100 | \n0.82 | \n2.0 | \n13.61 | \n
101–125 | \n0.70 | \n1.8 | \n12.24 | \n
126–150 | \n0.60 | \n1.1 | \n7.48 | \n
Calcium content in the soil and growth of sugarcane root system in a soil that received limestone and gypsum.
Source: adapted from [15].
Soil calcium and magnesium contents decrease during sugarcane cycles both by the removal of bases by harvests and by acidification caused by nitrogenous fertilizers. This effect is demonstrated in the long-term study (Figure 4) conducted by [15, 16]. These authors evaluated the reacidification of a soil cultivated with sugarcane by five cuts.
\nChanges in the base saturation of a soil cultivated with sugarcane. Source: adapted from [15, 16].
Initially, the soil presented, at the layers 0–20 and 20–50 cm, a base saturation of 15 and 7%, respectively. At the time of preparation of the soil for planting sugarcane, 2.5 t of limestone and 1.5 t of gypsum were applied per hectare. Soil chemical changes in plant cane and regrowth are shown in Figure 4. After plant cane thinning, base saturation at the layers 0–20 and 20–50 cm was, respectively, 52 and 38%; by the fifth cut, the values were similar to those observed at the time of reforestation.
\nThe authors of this chapter have recommended liming for regrowth areas when there is a base saturation of less than 50% at the 0–20 cm layer. The application of corrective should be in the total area preceding crop treatments and calculating the necessary amount as previously described.
\nThe mineral fertilization of sugarcane is based on the results of soil analysis at the 0–20 cm layer and on the productivity desired.
\nNitrogen is important for the nutrition and physiology of sugarcane because, among other functions, it is a constituent of all amino acids, proteins, enzymes, and nucleic acids [17]. Nitrogen and potassium are absorbed in greater amounts by this crop [3]. The absorbed nitrogen increases the meristematic activity of shoots, resulting in greater tillering and leaf area index (LAI). Furthermore, N increases leaf longevity. Such an increase in LAI increases the efficiency of use of solar radiation, measured as the fixation rate of carbon dioxide (μmol of CO2 m−2 s−1), thus increasing accumulation of dry matter.
\nThe accumulation of nitrogen by sugarcane varies according to cultivar, crop age, and availability of N and other elements in the soil solution and also depends on soil and climatic factors. For the more common varieties planted, nitrogen extraction ranges around 1.2 kg per ton of natural matter of shoots. Considering that roots and rhizomes correspond, on average, to 30% of the mass of the whole plant, it can be estimated that for each t of natural matter accumulated by shoots, there is an absorption of 1.5 kg of N by the plant. Therefore, for systems with a productivity greater than 120 tons of natural matter per hectare, the amount of N absorbed by the crop exceeds 180 kg ha−1. In these systems, the use of nitrogen fertilization at doses ranging from 60 to 100 kg ha−1 is suggested [1].
\nNitrogen uptake and nitrogen metabolism are greatly influenced by phosphorus availability. In plants with inadequate phosphorus supply, there is a decrease in the nitrate absorption of the soil solution. The nitrate translocation from roots to shoots decreases, thus increasing the accumulation of amino acids in leaves and roots. Ref. [18] observed an enormous influence of the availability of P, both of nutrient and endogenous solution, on corn nitrogen uptake and metabolism (Figure 4). Well-supplied phosphorus plants before and during a kinetic study (+P; +P) showed a practically constant nitrate absorption during the experiment. However, plants deprived of P before and during the experimental phase (−P; −P) were unable to absorb the nitrate from the solution.
\nIt is believed that plant cane, because it has a higher phosphorus supply when compared to regrowth, behaves similar to corn plants well supplied with phosphorus (+P; +P). In studies conducted by the authors in the region of Passos, southern Minas Gerais, it was verified that the increase in the dose of phosphorus applied to planting grooves affected larger accumulations of N in the biomass of plant cane, since for each kg of P applied there was an increase of about 1 kg of N. These results are certainly the effects of changes caused in the absorption and metabolism of nitrogen, as observed by [18].
\nIt should be noted, however, that some studies reported a low response of plant cane to nitrogen fertilization, and the causes of such low responses are not sufficiently explained. Several authors have attributed it to experimental variability, to mineralization of organic matter and of crop remains, to fertilizer application times, and to losses by leaching and denitrification [19, 20]. However, in an experiment conducted by [3] with plant cane cultivated in a sandy soil and fertilized with marked urea (15N), losses were not observed with the leaching of nitrogen from the fertilizer (Figure 5). The movement of the 15N-fertilizer was small. More than 70% of the fertilizer recovered in the soil was at the 0–30 cm layer. There was a measurable loss of N native from the soil, or of crop remains, equivalent to 4.5 kg ha−1 [3]. Thus, if nitrogen fertilization is applied to plant cane, nitrogen fertilizer, at doses ranging from 60 to 100 kg ha−1, should be applied to the bottom of planting grooves along with phosphorus and potassium.
\nNitrate uptake by corn plants with different phosphorus supplies: adequate before and during the study (+P; +P), adequate before and absent during the study (+P; −P), absent before and adequate during the study (−P; +P), and absent before and during the study (−P; −P). Source: adapted from [18].
The responses of sugarcane regrowth to nitrogen fertilization are more frequent than in plant cane, with a percentage above 90%. As a general recommendation, it is suggested to apply 1.0 kg of N per ton of natural matter accumulated in shoots. Since industrializable culms represent on average 80% of the natural matter of shoots, yields of 100 t of culms would correspond to 125 t of natural matter. In this case, the recommendation for fertilization would be 125 kg of N ha−1, and the nitrogen fertilizer should be applied in a single dose together with potassium.
\nUrea has been the most used nitrogen fertilizer for sugarcane fertilization mainly because of its lower cost per unit of N compared to other sources. The application of urea to the soil or straw may lead to large losses due to the volatilization of ammonia (approximately 40%) [1]. Therefore, it is recommended to bury it into the soil at a depth of approximately 7.0 cm. When it is not possible to bury the urea in the soil, it must be irrigated to incorporate it into the soil or to fertilize it before a rain, which is possible only in small areas. If it is not possible to bury urea in the soil, irrigate it, or fertilize it before a rain, one should choose ammoniacal sources, such as ammonium sulfate, or nitric sources.
\nThe highest dose of phosphorus should be applied to the bottom of planting grooves. Such application at a greater depth increases the nutrient uptake by sugarcane, since water availability at the subsurface varies less than on the surface. The mobility of phosphorus in the soil is small, and its diffusion is influenced by several factors, especially precipitation by cations such as iron, aluminum, and calcium; volumetric content of water in the soil; adsorption of phosphorus by soil colloids; complexity of the environment structure; soil compaction; distance to reach roots; and contents of elements in soil [21]. In general, very low values are recorded for transport of phosphorus due to its strong interaction with soil colloids, especially in very weathered soils. According to [21], it can be estimated that the transport is on average 0.013 mm per day.
\nEven applying a higher dose of phosphorus during planting, there is a need for phosphate fertilization for regrowth. Tables 3–5 present recommendations for phosphate fertilization of plant cane at the bottom of planting grooves, considering the extractor used in the soil chemical analysis, Mehlich or ion exchange resin, as well as soil fertility classes.
\nClay content (g kg−1) | \nLow | \nMedium | \nHigh | \n
---|---|---|---|
\n | Available phosphorus classification (mg dm−3) | \n||
0–150 | \nLess than 20 | \n20–30 | \nAbove 30 | \n
150–350 | \nLess than 15 | \n15–20 | \nAbove 20 | \n
350–600 | \nLess than 10 | \n10–15 | \nAbove 15 | \n
600–1000 | \nLess than 5 | \n5–10 | \nAbove 10 | \n
\n | Available potassium classification (mg dm−3) | \n||
\n | Less than 40 | \n41 a 90 | \nAbove 90 | \n
Soil fertility classes considering clay, phosphorus, and potassium contents extracted with Mehlich.
Production expectation in the cane plant cycle (t ha−1) | \nSoil fertility class | \n||
---|---|---|---|
Low | \nMedium | \nHigh | \n|
Dose of P (kg ha−1)* | \n|||
Less than 100 | \n70 | \n— | \n— | \n
100–150 | \n80 | \n60 | \n40 | \n
150–180 | \n90 | \n70 | \n50 | \n
Above 180 | \n100 | \n80 | \n60 | \n
Phosphorus doses suggested for sugarcane fertilization based on the availability of phosphorus extracted with Mehlich and on the expectation of natural matter production.
To convert P into P2O5, multiply the desired value by 2.29.
Production expectation in the cane plant cycle (t ha−1) | \nExtracted phosphorus (mg dm−3) | \n|||
---|---|---|---|---|
0–6 | \n7–17 | \n16–40 | \n>40 | \n|
Dose of P (kg ha−1)* | \n||||
Less than 100 | \n80 | \n44 | \n30 | \n20 | \n
100–150 | \n90 | \n55 | \n40 | \n26 | \n
Above 150 | \n100 | \n66 | \n45 | \n35 | \n
Phosphorus doses suggested for sugarcane fertilization based on the availability of phosphorus extracted with ion exchange resin and on the expectation of natural matter production.
To convert P into P2O5, multiply the desired value by 2.29.
Source: adapted from [8].
Production expectation in the cane plant cycle (t ha−1) | \nSoil fertility class | \n||
---|---|---|---|
Low | \nMedium | \nHigh | \n|
Dose of K (kg ha−1)* | \n|||
Less than 90 | \n100 | \n— | \n— | \n
90–120 | \n120 | \n100 | \n80 | \n
120–150 | \n140 | \n120 | \n100 | \n
150–180 | \n160 | \n140 | \n120 | \n
Above 180 | \n180 | \n160 | \n140 | \n
Potassium doses suggested for sugarcane fertilization based on the availability of potassium extracted with Mehlich and on the expectation of natural matter production.
To convert K into K2O, multiply the desired value by 1.20. When sugarcane is harvested for animal feed, it is suggested to raise the recommended K dose by 25%.
Production expectation in the cane plant cycle (t ha−1) | \nK extracted with resin (mmolc dm−3) | \n||||
---|---|---|---|---|---|
0–0.7 | \n0.8–1.5 | \n1.6–3.0 | \n3.1–6.0 | \n>6.0 | \n|
Dose of K (kg ha−1)* | \n|||||
Less than 100 | \n120 | \n100 | \n60 | \n60 | \n0 | \n
100–150 | \n160 | \n140 | \n100 | \n80 | \n0 | \n
Above 150 | \n200 | \n160 | \n120 | \n100 | \n0 | \n
Potassium doses suggested for sugarcane fertilization based on the availability of potassium extracted with ion exchange resin and on the expected production.
To convert K into K2O, multiply the desired value by 1.20.
Source: adapted from [8].
Regrowth production expectation (t ha−1) | \nK extracted with resin (mmolc dm−3) | \n||
---|---|---|---|
0–1.5 | \n1.6–3.0 | \n>3.0 | \n|
Dose of K (kg ha−1)* | \n|||
Less than 60 | \n90 | \n60 | \n30 | \n
60–80 | \n110 | \n80 | \n50 | \n
80–100 | \n130 | \n100 | \n70 | \n
Above 100 | \n150 | \n120 | \n90 | \n
Potassium doses suggested for regrowth fertilization based on the availability of potassium extracted with ion exchange resin and on the expected production.
To convert K into K2O, multiply the desired value by 1.20.
Source: adapted from [8].
Chemical composition | \nOrigin of must | \n||
---|---|---|---|
\n | Molasses | \nMixed | \nCane juice | \n
\n | kg of the element by m3 de vinasse | \n||
N | \n0.57–0.79 | \n0.33–0.48 | \n0.25–0.35 | \n
P | \n0.05–0.15 | \n0.03–0.14 | \n0.03–0.07 | \n
K | \n3.27–6.32 | \n1.81–2.78 | \n0.95–1.61 | \n
Ca | \n1.32–1.70 | \n0.40–0.95 | \n0.08–0.52 | \n
Mg | \n0.50–0.85 | \n0.19–0.35 | \n0.13–0.25 | \n
S | \n0.30–0.40 | \n0.45–0.54 | \n0.58–0.70 | \n
Organic matter | \n37.0–57.0 | \n19.1–45.1 | \n15.3–34.7 | \n
\n | g of the element by m3 de vinasse | \n||
Fe | \n52–120 | \n47–130 | \n45–110 | \n
Cu | \n3.1–9.3 | \n4.2–57.3 | \n1.0–18.0 | \n
Zn | \n3.0–4.0 | \n3.0–4.0 | \n2.0–3.0 | \n
Mn | \n6.0–11.0 | \n5.0–11.0 | \n5.0–10.0 | \n
pH | \n4.2–4.4 | \n3.6–4.4 | \n3.5–3.8 | \n
Chemical composition of vinasse originating from different musts.
Source: Analyses carried out by the authors on the vinasse of mills located in Minas Gerais and Alagoas, Brazil.
\n | Extractor | \n|||||||
---|---|---|---|---|---|---|---|---|
\n | DTPA | \nMehlich-1 | \n||||||
\n | Element | \n|||||||
Available | \nCu | \nZn | \nMn | \nFe | \nCu | \nZn | \nMn | \nFe | \n
\n | mg dm−3 | \n|||||||
Low | \n≤0.2 | \n≤0.5 | \n≤1.2 | \n≤4 | \n≤0.8 | \n≤1.0 | \n≤6 | \n≤19 | \n
Medium | \n0.3–0.8 | \n0.6–1.2 | \n1.3–5.0 | \n5–12 | \n0.8–1.2 | \n1.0–1.5 | \n6–8 | \n19–30 | \n
High | \n>0.8 | \n>1.2 | \n>5.0 | \n>12 | \n>1.2 | \n>1.5 | \n>8 | \n>30 | \n
Minimum values of micronutrient availability in the soil extracted with a solution of DTPA and Mehlich-1.
Source: cited by [1].
According to some authors, it is unlikely to obtain a productivity above 150 t when the phosphorus extracted with resin is lower than 6.0 mg dm−3. However, in studies conducted in newly developed Cerrado areas in the northwest of Minas Gerais on a phosphorus content lower than 6.0 mg dm−3, yields were higher than 200 tons of culms per hectare in a plant cane with a 14-month cycle fertilized with 100 kg of P per hectare and receiving complementary irrigation of only 120 mm [1].
\nPhosphorus applied during sugarcane planting ensures, in most cases, an adequate supply of this element to plant cane and the first regrowth. Formulations containing P in the fertilization of later regrowth should be used. Prior to phosphate fertilization, the soil should be analyzed at the 0–20 cm layer and, if the base saturation (V) is less than 50%, it is recommended to perform first a liming to raise the V to 60%. As shown in Table 1, the absence of exchangeable aluminum in the soil solution increases the efficiency of phosphate fertilization, especially since there is no formation of aluminum phosphate (a low solubility compound) in the soil and within plant roots. If the base saturation is greater than 50% and the P content, extracted with Mehlich, is lower than 10 mg/dm3, a regrowth phosphate fertilization is recommended.
\nThe dose of phosphorus used may be based on the recovery of the P removed by harvesting. In this case, for each ton of natural material, 200–300 g of P should be applied. If, for example, the production of natural regrowth material was 120 t per ha, which corresponds to about 100 t of industrializable culms, from 25 to 40 kg of P should be applied per ha. Phosphate fertilizer should be applied together with N and K. In large crops, regrowth N-P-K fertilization is carried out simultaneously with subsoiling and cultivation of interlines. In small and medium properties, especially those where burnt sugarcane is harvested or produced for animal feed, the furrowing of sugarcane lines using an animal traction plow for later fertilization has presented good results. The N-P-K fertilizer is applied to open grooves in sugarcane interlines and then covered with soil using animal traction.
\nPotassium fertilization of sugarcane is carried out at planting and after each sugarcane cut because potassium is displaced in the soil profile. The mineral fertilization of sugarcane is based on the results of soil analysis at the 0–20 cm layer, on the productivity desired and on the final use of sugarcane. In sugarcane fields intended for cattle feeding, the potassium dose to be applied should be increased, since nutrient removal will be greater because sugarcane is harvested along with nodes and dry leaves. The amount of potassium contained in nodes and dry leaves of sugarcane ranges around 70 kg per ha [22] and may reach 140 kg per ha in plant cane [3]. Tables 6–8 present the recommendations of potassium fertilization for plant cane and regrowth, with Mehlich or ion exchange resin as extractors.
\nThe dose of K to be applied to regrowth may be based on the recovery of the potassium removed by the crop, as suggested for nitrogen and phosphate fertilization. This method was adopted by the authors and has been recommended with excellent agronomic and financial results. Although the absorption and the removal of potassium vary among sugarcane cultivars, it can be considered that for each ton of natural matter harvested, there is, on average, a removal of 1.5 kg of K. There is no need to partition the potassium used in regrowth fertilization due to possible losses by leaching. In studies conducted by Oliveira et al. [3] using lysimeters, K losses by leaching were not reported (Figure 6). These results were confirmed by [23], who also observed that K losses by percolation below a depth of 100 cm were 9.0 kg ha−1, totally compensated by the input of K from rainwater (18 kg ha−1).
\nSolution volume and mass of percolated nitrogen during the plant cane cycle cultivated in a sandy soil.
Potassium chloride has been the most used source of K in fertilization. However, other residues containing potassium are also used, among them vinasse, a by-product of alcohol manufacture. Vinasse may replace potassium fertilization. Therefore, the amount of potassium supplied by application of vinasse should be fully deducted from mineral fertilization. The volume of vinasse applied ranged from 60 to 300 m3 ha−1 depending on the potassium concentration. The concentration of K in vinasse originating from molasses is higher than in others, followed by a mixed must, which contains on average twice as much K as in vinasse originating from sugarcane juice, with values ranging between 2.5 and 1.2 kg m−3, respectively (Table 9).
\nSulfur can be dispensed in areas that received application of vinasse or agricultural gypsum. The critical level of S-SO4−2 in the soil, extracted with Ca(H2PO4)2 500 mg L−1, is 10 mg/dl3. In areas in need of this macronutrient, at least 30 kg of sulfur per hectare should be applied using ammonium sulfate or simple superphosphate, which contains, respectively, approximately 210 and 110 g of S per kg of fertilizer (Figure 7).
\nSolution volume and mass of percolated potassium during the plant cane cycle cultivated in a sandy soil.
In most areas cultivated with sugarcane in Brazil, there has been an adequate supply of micronutrients in the soil, thus dispensing their use in chemical fertilizations. However, the implantation of sugarcane plantations in less fertile or marginal areas, associated with fertilization using concentrated fertilizers and the planting of high productivity varieties, which increasingly increase the absorption and export of nutrients, has caused micronutrient deficiency in several sugarcane plantations. In such cases, there is a need for the supply of microelements by fertilization. Soil analysis and area and variety history have been used as predictive methods for assessing the possibility of occurrence of micronutrient deficiency. Soil analysis should be associated to area and variety history since analytical results are influenced by the extractor used, by the characteristics of the soil and of the variety, and also by the time of sample collection. There are reports of marked effects of soil moisture on micronutrient contents [1, 5].
\nStudies carried out by [24] showed that the best correlations between the Zn or Cu contents in soils and the concentrations of these micronutrients in plants were obtained by the method that uses a solution of diethyl triamine penta-acetic acid (DTPA) as extractor when compared to Mehlich-1 and HCl extractors. According to [24], there is a tendency for DTPA to be more efficient than Mehlich-1 and HCl in situations where the availability of Zn and Cu is changed by liming. As for Mn, acid and chelating solutions have shown very close correlation coefficients between Mn in soil and in plants. However, by analyzing soils fertilized with Mn oxides, there was a tendency of DTPA being the best extractor.
\nTable 10 lists the minimum levels of micronutrient availability in soil extracted with DTPA and Mehlich-1 solution, below which such microelements should be supplied to plants by fertilization. The doses of copper, zinc, manganese, and iron to be applied, in case of deficiency, are 2.5–6.0, 5.0–7.0, 3.0–6.0, and 6.0–10.0 kg ha−1, respectively, using oxides, chlorides, and sulfates.
\nIn studies conducted by the authors on coastal plain soils in Alagoas, northeastern Brazil, it was verified that even when high-dose manganese and copper sulfates (up to 16.0 kg of element/ha) were applied, RB867515 and RB92579 remained deficient in these elements. The content of these nutrients in the +3 leaf limbus, used to evaluate nutritional status, was lower than 5.0 and 40.0 mg/kg of dry matter, respectively, for copper and manganese, characterizing a severe deficiency of these elements. The high adsorption of copper and manganese sulfates may have been the cause of the absence of responses. Ref. [25] studied the adsorption of copper originating from several compounds. These authors studied the application of CuSO4 to sandy and humic soils. They found a very high adsorption (99.4%) of copper 2 h after its addition to the soil. On the other hand, copper in the ethylene diaminotetraacetic acid and diaminocyclohexane tetraacetic acid forms presented a soil percentage adsorption of 7.3 and 5.3, respectively. Therefore, it is necessary to evaluate the efficiency of other sources of copper and manganese because the adsorption of copper and manganese sulfates by the soil was very high. In addition to compromising the productive potential of these varieties, copper and manganese deficiency leads to metabolic changes that compromise the quality of the broth. These nutrients are constituents of the polyphenol oxidase and amylase metalloenzymes [17, 26, 27]. Therefore, with a poor performance of these enzymes, there is accumulation of phenolic and starch compounds.
\nThe chemical analysis of sugarcane leaves is another way for evaluating the nutritional status of crops. The preference for leaves is because, in general, they reflect better the variations in the supply of nutrients both by the soil and by fertilizations. In sugarcane, it has been recommended to collect the +2 or +3 leaves. The leaf +1 is, in the descending direction of the stem, the first leaf to show a fully visible ligule (region of insertion of the leaf sheath on the stem). For the chemical analysis, the median third of the +2 or +3 leaf is used excluding the central vein.
\nSamples from the middle third should first be washed in clean running water and then in distilled water. Then, the material should be dried at 65°C until constant weight. If this drying is not possible, the samples should be sent quickly to the laboratory where they will be analyzed. Table 11 lists the nutrient concentration ranges considered adequate according to Brazilian researchers.
\nGreen fertilization is the cultivation of plants for the purpose of incorporating them into the soil. Among the desirable characteristics of a plant to be used as green manure, we may mention the possibility of mechanization from sowing to seed harvesting, absence of dormant seeds, vigorous and deep root system, ability to associate with nitrogen fixing bacteria in atmospheric air, fast growth to control weeds, and presence of mechanisms or synthesizing compounds that aid in the control of pests, such as nematodes, and diseases.
\nSeveral legumes have these characteristics, but generally there is a preference for Crotalaria juncea in the Center-South region of Brazil and for Crotalaria spectabilis in the states of Alagoas and Pernambuco, northeastern Brazil. Crotalaria juncea is a legume with a very fast initial growth, which provides it with a great competition potential with weeds. However, it is very sensitive to nictoperiods, early blooming in growing nights and, consequently, interrupting growth. Therefore, when cultivating for green manure, sowing should be performed in early October, or as soon as possible. However, for seed production, it should be sown in March.
\nIn studies conducted by [1] in two regions of Minas Gerais, Alto Paranaíba and Zona da Mata, there was accumulation of dry matter (DM) by Crotalaria juncea sown in October, around 15 tons per hectare, with nitrogen concentration oscillating around 20 g of N per kg of DM. Thus, for a DM yield of 15 t ha−1, the amount of N fixed and/or recycled is 300 kg per hectare. In areas densely infested with Brachiaria plantaginea, the inclusion of Crotalaria in the system increased the mass of N over the soil by 320% since the accumulation by the natural vegetation of the fallow area was 66 kg of N per ha, while in the area with Crotalaria, this accumulation exceeded 250 kg ha−1, a sufficient quantity to ensure a production of 230 t of natural matter of sugarcane per hectare. Ref. [1] reported that in experiments conducted in areas where Crotalaria was incorporated into the soil, there was an increased productivity in plant cane of 15 t of culms per hectare compared to fallow areas.
\nThe dry matter production of Crotalaria juncea and spectabilis in the states of Alagoas and Pernambuco oscillated around 4.5 t of DM per ha. This low production of DM, compared to that observed in the Center-South region, is mainly because the sowing season occurred at the beginning of the rainy season, between April and early May, therefore in longer nights. In Alagoas, in areas where Crotalaria spectabilis is used as green manure, it has been common to perform direct grooving without previous soil plowing, similar to the minimum cultivation systems adopted for some other crops.
\nStraw is the main crop residue. There are also several types of waste from the industrialization of sugarcane, among them vinasse, filter cake, boiler ashes, and bagasse, which are routinely used in fertilization as sources of nutrients and organic matter. The amount of straw that remains on the soil after the harvest of sugarcane not debrided with fire varies according to cultivar and adopted agricultural practices; such amount ranges from 12 to 18 t ha−1 [22]. In studies conducted by [22] in the region of Ribeirão Preto, SP, it was verified that, among the nutrients in straw, only potassium presented a great liberation during 1 year of permanence of this crop residue in field (Table 12). Thus, with the exception of K, the nutrients contained in straw will not contribute significantly to the nutrition of sugarcane during the cycle following the cut.
\nVinasse and filter cake are the main residues of cane industrialization. Vinasse, which has potassium, calcium, and organic matter as main constituents, is generally used for regrowth fertilizations and may, as discussed above, provide all the K for cultivation. According to the origin of the vinasse, the concentrations of the elements may vary, and chemical analyses must be conducted before its application. However, in general, the concentration of K in the vinasse originating from mixed must is, on average, twice as higher as that obtained from broth, with values ranging from 2.5 and 1.2 kg m−3, respectively.
\nFilter cake has a high percentage of moisture (approximately 75%), and average levels of P and Ca vary, respectively, from 5.0 to 10 and from 15 to 36 kg per ton of dry matter. It is used mainly in plant cane fertilization, applied at the bottom of the planting groove at an average dose of 30 t of natural matter per ha, or in total area at twice the dose. Considering an application of 40 t of natural filter cake per ha, around 10 t dry matter, with an average content of 7.0 kg of P per t of dry matter, there is a contribution of 70 kg of P per ha, dispensing phosphate fertilization at the time of planting for most soils.
\nThe composting of organic residues, mainly of sugarcane bagasse, is one more option for the use of such residues in the fertilization of sugarcane and in the improvement of the physical and chemical properties of the soil. The authors evaluated the technical and economic feasibility of using organic compounds based on sugarcane bagasse in sugarcane plantation. The research was conducted in soils with a great physical heterogeneity and a high capacity of phosphorus adsorption. Different mixtures of sugarcane bagasse and chicken litter were tested, ranging from 100 kg of bagasse to 80 kg of bagasse +20 kg of chicken litter, plus 5.0 kg of ammonium sulfate. After the composting process, 15 t of material per hectare were applied to the bottom of sugarcane planting grooves. The fertilizer 06-30-24 was distributed over the compound at a dose of 500 kg per hectare. The results showed that the compound presenting the greatest productivity was the mixture of 100 kg of bagasse +5.0 kg of ammonium sulfate, resulting in an increase of 55 tons of culms per hectare compared to the treatment that received only chemical fertilization. The cost of production and the application of the compound were equivalent to 23.5 tons of culms, and the use of this compound allowed a net gain of 31.5 tons of culms per hectare. The results obtained in this study showed that even though sugarcane bagasse is a nutrient-poor residue, its effect on soil physical properties, especially aeration and water retention capacity, resulted in a higher productivity increase than that verified for compounds richer in nutrients. However, it also mineralized faster.
\nAuthors | \nNutrient (g kg−1) | \n|||||
---|---|---|---|---|---|---|
\n | N | \nP | \nK | \nCa | \nMg | \nS | \n
[17]* | \n19–21 | \n2.0–2.4 | \n11–13 | \n8.0–10 | \n2.0–3.0 | \n2.5–3.0 | \n
[17]** | \n20–22 | \n1.8–2.0 | \n13–15 | \n5.0–7.0 | \n2.0–2.5 | \n2.5–3.0 | \n
[28] | \n18–25 | \n1.5–3.0 | \n10–16 | \n2.0–8.0 | \n1.0–3.0 | \n1.5–3.0 | \n
[29] | \n16–25 | \n2.0–3.5 | \n6–14 | \n4.3–7.6 | \n1.1–3.6 | \n1.3–2.8 | \n
Authors | \nNutrient (mg kg−1) | \n|||||
---|---|---|---|---|---|---|
\n | B | \nCu | \nFe | \nMn | \nMo | \nZn | \n
[17]* | \n15–50 | \n8–10 | \n200–500 | \n100–250 | \n0.15–0.30 | \n25–50 | \n
[17]** | \n— | \n8–10 | \n80–150 | \n50–125 | \n— | \n25–30 | \n
[28] | \n10–30 | \n6–15 | \n40–250 | \n25–250 | \n0.05–0.20 | \n10–50 | \n
[29] | \n6–29 | \n9–17 | \n76–392 | \n73–249 | \n— | \n— | \n
Nutrient concentration ranges in the middle third of the +2 or +3 leaf considered adequate.
Concentration ranges for plant cane.
Concentration ranges for regrowth.
Year | \nDM (t ha−1) | \nNutrient (kg ha−1) | \n||||||
---|---|---|---|---|---|---|---|---|
\n | \n | N | \nP | \nK | \nCa | \nMg | \nS | \nC | \n
1996 | \n13.9 a | \n64 a | \n6.6 a | \n66 a | \n25 a | \n13 a | \n9 a | \n6.255 a | \n
1997 | \n10.8 b | \n53 a | \n6.6 a | \n10 b | \n14 | \n8 b | \n8 a | \n3.642 b | \n
Year | \nStructural carbohydrates (kg ha−1) | \n||||||
---|---|---|---|---|---|---|---|
\n | Hemicellulose | \nCellulose | \nLignin | \nCell content | \nC/N | \nC/S | \nC/P | \n
1996 | \n3.747 a | \n5.376 a | \n1.043 a | \n3.227 a | \n97 a | \n695 a | \n947 a | \n
1997 | \n943 b | \n5.619 a | \n1.053 a | \n2.961 b | \n68 b | \n455 b | \n552 b | \n
Mass of dry matter (DM), amount of nutrients and structural carbohydrates in the samples of freshly harvested sugarcane straw without burning (1996) and in the remaining straw 1 year later (1997).
Source: Oliveira et al. [22]. Values followed by the same letter are not significantly different (Tukey’s test) at the 0.05 level.
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\n\nIf it is felt necessary to make changes to the list of Authors after a manuscript has been submitted or published, it is the responsibility of the Author concerned to provide a valid reason to amend the published list. Additionally, all listed Authors must verify and approve the proposed changes in order for any amendments to be made.
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\n\nAuthors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
\n\nPolicy last updated: 2017-05-29
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