**3. Concept of Chronodisruption**

The concept of chronodisruption was coined in 2003 by Thomas C. Erren, Russel J. Reiter and Claus Piekarski from the University of Cologne [24] (Figure 2). The term was meant to go beyond the concept of chronodisturbance, a general term they proposed to refer to modulations of rhythms over time that are not necessarily deleterious since physiological compensations may prevent the development of chronic disease resulting from altered rhythms. Chronodisturbance itself was a conceptual leap from more common concepts such as "circadian disruption" or "disruption of circadian rhythms" that suggest that rhythms over 24 h can become desynchronized and that this may have adverse health effects, since these common terms may be more limited in time scope than chronodisturbance which may have a decade scope. Thus, circadian disruption may be caused by travel across several time zones, however, within a limited period of time within this new time zone, adaptation of the circadian rhythms to the new time zone occurs and there are no long-term consequences. By contrast, chronic work in night shifts will lead to chronodisturbance, that is to persistent desynchronization between time and activity. In 2009, they further elaborated on the chronodisruption concept, stating that "chronodisruption can be understood as a critical loss of time order, i.e., a disorder or chaos of an otherwise physiological timing at different organizational levels, including the gene expression levels in individual cells" and thus, it is "a breakdown of phasing internal biological systems appropriately relative to the external, i.e., environmental changes, which leads to chronobiological disorders" [25]. Following with the chronic night shift example, this would be considered chronodisturbance as long as there are no adverse consequences for health, and chronodisruption if this leads to adverse consequences for health. Furthermore, they characterized chronodisruptors as "exogenous and endogenous exposures or effectors which are chronobiologically active and can thus disrupt the timing and order, i.e., the temporal organization of physiologic functions and hierarchies" [25]. A clear example of a chronodisruptor is the use of artificial light or backlit screens during the night. They

additionally proposed that assessment of melatonin levels in saliva, urine and blood may be a robust biomarker of chronodisruption. While in some fields the concept was immediately grasped (In 2007, the International Agency for Research on Cancer classified shift-work that involves circadian disruption as probably carcinogenic to humans), it was not until 2013 that the term chronodisruption was used in the context of CKD [26] and only in 2019 was a second manuscript published on the topic [27].

**Figure 2.** Concepts of circadian disruption, chronodisturbance and chronodisruption. As compared to a normal circadian rhythm, circadian disruptions are characterized by altered circadian rhythm that may be short or long lived. Chronodisturbance is a chronic disruption of circadian rhythms that somehow leads to adaptive phenomena that limit its negative impact. Chronodisruption is a chronic disruption of circadian rhythms that results in disease. Chronodisruptors (not shown) are the factors driving chronodisruption. The normal circadian rhythm is shown as a green line in the left panel and as a discontinuous line in the other panels. A red line represents the altered circadian rhythm in the three left panels. Please note the different timelines shown in the horizontal axis, with chronodisturbance and chronodisruption implying chronicity.

While this is surprising given the chronic nature of CKD, its similarities with aging and the widely known fact that circadian rhythms may be disturbed in CKD, it does not mean that the nephrological community is not aware of disruption of circadian rhythms in CKD. Indeed, very active research is going on as attested by recent reviews [14,15,26,28,29]. However, CKD researchers may benefit from a wider use of the terms and concepts of chronodisruption and chronodisruptor. Thus, the mere concept of chronodisruptor may facilitate the search of chronodisruptors involved in CKD manifestations. These may potentially be abnormal levels of uremic toxins or abnormally low levels of uremia-related factors, among others.


**1. Introduction**

Conservation practices such as the utilization of a no-till crop system or cover crops can be used to improve soil health, reduce soil loss, and mitigate net greenhouse gas (GHG) emissions. No-till is the practice of refraining from tilling the soil from harvest of the previous crop to the harvest of the current crop. Cover cropping involves planting a crop for seasonal cover. For example, numerous U.S. farms plant annual or cereal ryegrass in the fall to provide cover during the winter months.

No-till and cover crop practices vary widely among farms. Using U.S. Department of Agriculture-Economic Research Service (USDA-ERS) survey estimates, 27 percent of corn in 2016, 40 percent of soybeans in 2012, and 45 percent of wheat in 2017 utilized no-till cropping systems [1]. Though cover cropping is not as common as notill systems, its use has been growing. In 2017, 5.1 percent of U.S. acres utilized cover crops [2]. Moreover, with the recent interest in carbon farming, a carbon sequestering change made to some farm practices in exchange for receiving payments for carbon credits, the interest in no-till and cover crop practices has increased.

Given their obvious conservation benefits, why have management practices such as no-till and cover crops not been more widely adopted? As with any new system or technology, the benefits have to be weighed against the costs. Benefits may include an improvement in soil health, a reduction in soil loss, and mitigation of net greenhouse gas emissions. Costs may include extra costs associated with the practice (e.g., planting a cover crop), potential reductions in crop yields, and potential increase in net return risk (i.e., increased variability or downside risk of net returns resulting from the adoption of a conservation practice), because there are numerous benefits and costs, it is important to examine tradeoffs between key variables such as net returns, risk, soil health, and net greenhouse gas emissions. In other words, it is seldom possible to adopt a conservation practice that has higher net returns and less risk, and that improves soil health or reduces net greenhouse gas emissions.

The objective of this chapter is to examine the relationship between crop net returns, risk, and conservation practices. The conceptual framework developed can be used to explore crop net returns, risk, soil loss, and greenhouse gas emissions for different conservation practices.

### **2. Previous research**

Given the importance of the no-till and cover crop rotations to the conceptual framework developed in this study, the literature review in this section will briefly discuss previous studies that have examined the impact of tillage practices on net returns, soil health, and net greenhouse gas emissions, and the costs and benefits of cover crops. These discussions will be followed by a brief discussion of carbon markets.

Wade et al. [3] summarized the adoption of conservation practices that could be used to reduce the environmental impact of crop production, improve soil health, and reduce net greenhouse gas emissions. Though practices varied widely among crops and regions, approximately 40 percent of the combined acreage of corn, soybean, wheat, and cotton utilized no-till or strip-till practices. Strip-till is a tillage system that utilizes tillage only in narrow strips where seed are planted. In another USDA-ERS study, Claassen et al. [1] noted that no-till and strip-till used in conjunction with conservation rotations and cover crops have a multitude of soil health benefits. The authors noted that practices varied widely among crops and locations. They noted in general, however, that 70 percent of soybean, 65 percent of corn, 67 percent of wheat, and 40 percent of cotton acres used conservation tillage practices, which included no-till, strip-till, and mulch-till. Mulch-till refers to a technique in which organic materials, such as crop residues or mulch, are left on the soil surface and incorporated into the top layer of soil through tillage. The purpose of mulch-till is to improve soil fertility, moisture retention, and weed suppression while reducing soil erosion.

Bergtold and Sailus [4] edited a book that discussed the production, profitability, and stewardship involving conservation tillage systems in the southeast U.S. One of the chapters in the book focused on conservation economics [5]. The authors of this chapter indicated that in order to achieve goals related to maximizing profit and environmental stewardship requires an understanding of tools such as partial budgeting and/or enterprise budgets.

Practices such as the adoption of a no-till crop rotation often involve a learning curve. This fact was confirmed by Cusser et al. [6] who compared the development and stability of a no-till crop rotation with a conventional till system over a 29-year period. The authors indicated that it took over a decade to ascertain the impacts of the no-till system, underscoring the importance of long-run studies.

#### *Examining the Relationship between Crop Net Returns, Risk, and Conservation Practices DOI: http://dx.doi.org/10.5772/intechopen.112263*

Precision Conservation Management [7] summarized gross revenue, production costs, soil loss, and greenhouse gas (GHG) emissions for various tillage systems including no-till, strip-till, 1-pass light, 2-pass light, 2-pass moderate, and 2+ passes. The "light" passes utilized low disturbance tillage. Operator and land return were higher for the 1-pass light, 2-pass light, and 2-pass moderate systems than it was for no-till, strip-till, and 2+ passes. In particular, the return for the no-till system was from \$19 to \$30 lower than the return for the light and moderate pass systems. However, soil loss and GHG emissions were lower for the no-till system than they were for other tillage systems examined.

Next, we will turn our attention to previous research pertaining to cover crops. Plastina et al. [8] used partial budgets to assess the economic return to cover crop use on Midwest U.S. row crop farms. Essentially, the authors compared returns and costs for farms that utilized cover crops to those that did not utilize cover crops. The economic loss accruing to use of cover crops terminated with herbicides in a corn/ soybean rotation was \$12 per acre with the inclusion of cost-share payments and \$43 per acre without the inclusion of these payments. Plastina et al. [9] used focus groups and a partial budgeting framework to compare the net returns of crop systems with and without the use of cover crops. The net change in profits associated with cover crop use was a negative \$54 per acre.

Myers et al. [10] note that additional net returns generated from the adoption of cover crops is difficult to discern with one-year budget analysis. The authors noted that some farms have a very long-term positive experience associated with the use of cover crops. Cover crops were found to provide a faster payment when herbicideresistant weeds are a problem, cover crops are grazed, soil compaction is an issue, cover crops are used to speed up and ease the transition to no-till, soil moisture is at a deficit, fertilizer costs are high, and incentive payments are available.

A recent USDA-ERS publication noted that the share of harvested cropland with cover crops increased from 3.4 percent in 2012 to 5.1 percent in 2017 [2]. This study noted that unlike no-till and conservation tillage, cover crops involve increased costs, particularly in the short term. A recent survey by the Conservation Tillage Information Center [11] indicated that cover crops are typically combined with a no-till system. Approximately 12 percent of those surveyed went from not using cover crops to using cover crops on some of their acres between 2015 and 2019. Over half of the respondents used cover crops on at least 40 percent of their crop acres.

Gross revenue, production costs, soil loss, and GHG emissions for a no cover crop system and two cover crop systems, overwintering and winter terminal, are summarized by Precision Conservation Management [7] and Sellars et al. [12]. Operator and land return for the overwintering and winter terminal cover crop systems used for corn production (soybean production) were \$32 (\$44) and \$12 per acre (\$21 per acre) lower than the return under the no cover crop system, respectively. As with the utilization of a no-till system, soil loss and GHG emissions were reduced by utilizing the cover crop systems.

The potential to sequester carbon in agricultural soils has increased public and private interest in markets that pay farmers to sequester carbon using various management practices. Two common management practices discussed are the adoption of a no-till system or the use of cover crops. In their review of the literature, Havens et al. [13] noted that the average implementation cost for no-till was \$17 per acre and that the average implementation cost for cover crops was \$31 per acre. On average, sequestration resulted from the adoption of a no-till system and cover crops would be 0.77 and 0.76 metric tons per acre. In addition to discussing the average sequestration and average cost per acre, the authors estimated that the social benefit of sequestration would be \$51 per metric ton of CO2, which would more than offset the expected cost of implementing no-till or cover crop systems.

Thompson et al. [14] reported recent survey results pertaining to farm adoption of carbon contracts. Of the survey respondents, 39 percent were aware of opportunities to receive payments for capturing carbon, 7.1 percent had actively engaged in discussions regarding receiving payments for capturing carbon, and 1.3 percent had signed a contract to capture carbon. In a follow-up question, reasons preventing producers from enrolling, in order of percentage responding affirmatively, were payment level offered that was not large enough, potential legal liability, skepticism of carbon sequestration viability, and previous use of eligible practices were not covered under the contract.

In a series of Ag Decision Maker articles, Plastina [15], Plastina and Jo [16], and Plastina and Wongpiyabovorn [17] discuss carbon markets; how data, payments, and methods are made in carbon programs; and describe a tool that can be used to examine the potential net returns for carbon farming. Net returns are dependent on farming practices, contract type, contract length, frequency of additional practice implementation, contract price for each practice, expected change in contract price, farm area enrolled, participation in cost share programs, expected changes in costs, and discount rate. Plastina [15] noted that programs typically require additionality to generate payments. Additionality means that farmers must do something different or adopt a new practice to receive carbon payments.


2. Thermodynamics and kinetics of nanoemulsion

#### 2.1 Free energy diagram

The thermodynamic stability of a particular system is governed by the change in free energy between it and an appropriate reference state. Nanoemulsion is thermodynamically unstable, which means that the free energy of nanoemulsion is

higher than the free energy of the separate phases (oil and water). For microemulsion, this condition is opposite. To understand the molecular basis of the free energy difference, let us consider that a system (nano-/microemulsion) exists in equilibrium between the initial and final states. The free energy change associated with the formation of the dispersion consists of an interfacial free energy term (ΔGI) and a configuration entropy term (�TΔSconfig) [9]:

$$
\Delta \mathbf{G}\_{\text{formation}} = \Delta \mathbf{G}\_{I} - T \Delta \mathbf{S}\_{\text{config}} \dots
$$

The free energy to increase the contact area (ΔA) at the interface is ΔGI ¼ γΔA, which is always positive; consequently, this term always opposes the formation of the dispersions. However, the interfacial tension (γ) depends on the curvature of the surfactant layer-decreasing as the curvature approaches to its optimum value. A phenomenological description of the dependence of interfacial tension on droplet curvature can be formulated as [10–12]:

$$
\lambda = \lambda\_0 + (\lambda\_\infty + \chi\_0) \frac{(R\_0 - R)^2}{R\_0^2 + R^2},
$$

can assume that thermodynamically stable dispersion can be achieved when droplet radius is close to the optimum (R = R0) and hence the interfacial free energy that

Predicted variation of free energy change with droplet radius for the formation of emulsion state assuming (a) constant interfacial tension and (b) varying interfacial tension with curvature. Taken from Reference [3].

Kinetic stability, as to be contrasted with the thermodynamic stability, is determined by two important factors [3]: (1) Energy barriers: any energy barrier

(or activation energy) that separates the two states (final and initial) will determine the rate of the conversion of one state to another. The height of this energy barrier depends on the forces operating in close proximity of two droplets, such as repulsive hydrodynamic and colloidal (steric and electrostatic) interactions [13]. Nanoemulsion can be made kinetically stable by introducing sufficiently

large energy barrier (typically >20 kT) between the two states, while for

in a nanoemulsion are particularly labile toward the growth over time by a

micelle. In mechanism three, a large number of oil molecules are released from the oil droplets collectively with excess of surfactant molecules to form

microemulsion, there is still an activation energy required (in terms of mechanical agitation or heating the system) to reach the thermodynamically stable state after the components are brought into contact. (2) Mass transport phenomena: droplets

process known as "Ostwald ripening" [8], in which solute molecules (or mass) are exchanged between the droplets via molecular diffusion through the solvent. Three alternative mechanisms have been proposed in the literature suggestive of that micelle which plays an important role in facilitating the mass exchange between the droplets by acting as carriers of oil molecules [8]. In mechanism one, oil molecules are transferred via direct micelle collisions, i.e., the rate is directly proportional to the volume fraction of micelle in solution. Numerous studies indicate a higher rate of mass transport above the CMC of the surfactant used. The lack of such CMC dependence for ionic surfactants however, may stem from the electrostatic repulsion between the droplet and micelle. In mechanism two, oil molecules exit the droplet, are exposed to the continuous phase and are soon captured by micelles in the immediate vicinity of the droplet. For non-ionic surfactant micelles, the higher rate of mass transfer is expected due to their higher solubilization capacity and the absence of electrostatic repulsion between the droplet and uncharged

otherwise opposes the emulsification can be appreciably reduced.

2.2 Kinetic picture

Importance of Surface Energy in Nanoemulsion DOI: http://dx.doi.org/10.5772/intechopen.84201

Figure 2.

a new micelle.

91

where γ∞ and γ0 are, respectively, the interfacial tension values at the planar O/W interface and when the surfactant layer reaches its optimum curvature. R0 is the droplet radius at the optimum curvature.

On the other hand, configuration entropy (ΔSconfig) which depends on the number of arrangements accessible to the oil phase in an emulsified state is much greater than that in a non-emulsified state, and therefore it always favors the formation of the dispersion. An expression for the ΔSconfig can be derived from the statistical analysis [9]:

$$
\Delta \mathbf{S}\_{\rm config} = -\frac{nk}{\phi} \left( \phi ln \phi + (\mathbf{1} - \phi) \ln \left( \mathbf{1} - \phi \right) \right),
$$

where k is the Boltzmann constant, n is the number of droplets and ϕ is the disperse-phase volume fraction.

The plot for the different free energies with droplet radius assuming that the interfacial tension is the same as that for planer O/W interface is shown in Figure 2(a) [3]. It can be observed from the figure that the interfacial free energy contribution increases (hence unfavorable) with decrease in droplet size (increase in interfacial area), while the configuration of free energy becomes progressively negative (hence favorable) with decrease in droplet size (increase in number of different ways that droplet can be organized). The total free energy, however, becomes increasingly positive with decrease in droplet size, since the interfacial free energy term dominates the configuration entropy term.

This implies that the formation of nanoemulsion becomes increasingly thermodynamically unfavorable as the radius of the droplets fall and where the interfacial tension is similar to that at a planar surface. Interestingly, if the calculation were performed assuming the dependence of the interfacial tension on the curvature, the situation becomes more complex [3]. As shown in Figure 2(b), with decrease in droplet radius from say, 1000 nm, there is an increase in interfacial free energy. But once the droplet becomes smaller below a certain radius, the interfacial free energy decreases and reaches a minimum value, before rising again with further decrease in radius. The interfacial free energy contribution still is the dominant factor, forcing the total free energy to attain a minimum value that is close to the optimum curvature of surfactant layer. Thus, under this consideration, one

Importance of Surface Energy in Nanoemulsion DOI: http://dx.doi.org/10.5772/intechopen.84201

#### Figure 2.

higher than the free energy of the separate phases (oil and water). For microemulsion, this condition is opposite. To understand the molecular basis of the free energy difference, let us consider that a system (nano-/microemulsion) exists in equilibrium between the initial and final states. The free energy change associated with the formation of the dispersion consists of an interfacial free energy term (ΔGI) and a

ΔGformation ¼ ΔGI � TΔSconfig :

γ ¼ γ0 þ γ∞ þ γ0 ð Þð Þ R0 � R 2

where γ∞ and γ0 are, respectively, the interfacial tension values at the planar O/W interface and when the surfactant layer reaches its optimum curvature. R0 is the

On the other hand, configuration entropy (ΔSconfig) which depends on the number of arrangements accessible to the oil phase in an emulsified state is much greater than that in a non-emulsified state, and therefore it always favors the formation of the dispersion. An expression for the ΔSconfig can be derived from the statistical

where k is the Boltzmann constant, n is the number of droplets and ϕ is the

This implies that the formation of nanoemulsion becomes increasingly thermodynamically unfavorable as the radius of the droplets fall and where the interfacial tension is similar to that at a planar surface. Interestingly, if the calculation were performed assuming the dependence of the interfacial tension on the curvature, the situation becomes more complex [3]. As shown in Figure 2(b), with decrease in droplet radius from say, 1000 nm, there is an increase in interfacial free energy. But

once the droplet becomes smaller below a certain radius, the interfacial free energy decreases and reaches a minimum value, before rising again with further decrease in radius. The interfacial free energy contribution still is the dominant factor, forcing the total free energy to attain a minimum value that is close to the optimum curvature of surfactant layer. Thus, under this consideration, one

The plot for the different free energies with droplet radius assuming that the interfacial tension is the same as that for planer O/W interface is shown in Figure 2(a) [3]. It can be observed from the figure that the interfacial free energy contribution increases (hence unfavorable) with decrease in droplet size (increase in interfacial area), while the configuration of free energy becomes progressively negative (hence favorable) with decrease in droplet size (increase in number of different ways that droplet can be organized). The total free energy, however, becomes increasingly positive with decrease in droplet size, since the interfacial free

R2 0 þ R2 ,

ϕ ð Þ ϕlnϕ þ ð Þ 1 � ϕ ln 1ð Þ � ϕ ,

The free energy to increase the contact area (ΔA) at the interface is ΔGI ¼ γΔA, which is always positive; consequently, this term always opposes the formation of the dispersions. However, the interfacial tension (γ) depends on the curvature of the surfactant layer-decreasing as the curvature approaches to its optimum value. A phenomenological description of the dependence of interfacial tension on droplet

configuration entropy term (�TΔSconfig) [9]:

Nanoemulsions - Properties, Fabrications and Applications

curvature can be formulated as [10–12]:

droplet radius at the optimum curvature.

disperse-phase volume fraction.

ΔSconfig ¼ � nk

energy term dominates the configuration entropy term.

analysis [9]:

90

Predicted variation of free energy change with droplet radius for the formation of emulsion state assuming (a) constant interfacial tension and (b) varying interfacial tension with curvature. Taken from Reference [3].

can assume that thermodynamically stable dispersion can be achieved when droplet radius is close to the optimum (R = R0) and hence the interfacial free energy that otherwise opposes the emulsification can be appreciably reduced.

#### 2.2 Kinetic picture

Kinetic stability, as to be contrasted with the thermodynamic stability, is determined by two important factors [3]: (1) Energy barriers: any energy barrier (or activation energy) that separates the two states (final and initial) will determine the rate of the conversion of one state to another. The height of this energy barrier depends on the forces operating in close proximity of two droplets, such as repulsive hydrodynamic and colloidal (steric and electrostatic) interactions [13]. Nanoemulsion can be made kinetically stable by introducing sufficiently large energy barrier (typically >20 kT) between the two states, while for microemulsion, there is still an activation energy required (in terms of mechanical agitation or heating the system) to reach the thermodynamically stable state after the components are brought into contact. (2) Mass transport phenomena: droplets in a nanoemulsion are particularly labile toward the growth over time by a process known as "Ostwald ripening" [8], in which solute molecules (or mass) are exchanged between the droplets via molecular diffusion through the solvent. Three alternative mechanisms have been proposed in the literature suggestive of that micelle which plays an important role in facilitating the mass exchange between the droplets by acting as carriers of oil molecules [8]. In mechanism one, oil molecules are transferred via direct micelle collisions, i.e., the rate is directly proportional to the volume fraction of micelle in solution. Numerous studies indicate a higher rate of mass transport above the CMC of the surfactant used. The lack of such CMC dependence for ionic surfactants however, may stem from the electrostatic repulsion between the droplet and micelle. In mechanism two, oil molecules exit the droplet, are exposed to the continuous phase and are soon captured by micelles in the immediate vicinity of the droplet. For non-ionic surfactant micelles, the higher rate of mass transfer is expected due to their higher solubilization capacity and the absence of electrostatic repulsion between the droplet and uncharged micelle. In mechanism three, a large number of oil molecules are released from the oil droplets collectively with excess of surfactant molecules to form a new micelle.