**Appendix A**

(Note that *E* T2 ¼ 1, implies that assumed variance components are correct and

*Validation of the VMM MD solution by Eq. (29) and CRUST19 model. (the scale is unitless).*

The study of the Moho discontinuity has been a crucial topic in inferring the dynamics of the Earth's interior for a long time. In general, the Moho can be studied with profitable results through seismic data. However, due to the sparsity of seismic data in parts of the world, it has not been well determined. With the advent of satellite missions, it has been possible to recover the Moho constituents via satellite

So far, various isostatic models have been presented for recovering the Moho constituents, but it was not clarified which one is most appropriate to employ for geophysical and geodynamical purposes. The preliminary and simplest isostatic models proposed are the classical ones with local or regional compensation. However, those models cannot realistically image the actual Moho undulation. This is because they assume a uniform crustal density, disregarding the density irregularities distributed within the crust and sub-crust. Understanding this important role of Moho recovery has been in the center of the discussions by many geoscientists

Here we have determined the Moho constituents and their uncertainties based on the VMM technique using both gravimetric and seismic data on a global scale to a resolution of 1° � 1°. The combination of the gravimetric and seismic data in one approach as well as the joint adjustment of MD and density contrast are expected to

The basic VMM method is based on the hypothesis that the isostatic gravity disturbance vanish. However, this is the case only if the gravity component is reduced such that there are no signals from the Earth's interior below the crust. The major problem in this reduction is therefore to distinguish and remove those signals, which we utilize by estimating and removing the NIEs with the help from

The second step is to combine the gravimetric data, propagated in the VMM technique to a linear equation (with MD and MDC as the unknowns), with a seismic model, CRUST1.0. This is performed by a weighted least-squares adjustment, block by block, which has the advantage that the standard error of the unknowns can also be estimated block-wise. The weights of the gravity disturbances were based on the error estimates by Eq. (23), while the weights for CRUST1.0 data were those

the expected MDs of the two models are the same).

*Geodetic Sciences - Theory, Applications and Recent Developments*

gravity observations based on an isostatic model.

**6. Discussion and final remarks**

**Figure 4.**

during the last decades.

CRUST1.0 seismic model.

published in [12].

**36**

significantly improve the total result.

Let us assume that the compensation attraction in Eq. (2) is generated by a density contrast Δ*ρ* between the constant reference depth *D*0 and the actual depth *D*. Assuming that the density contrast may change only laterally, it follows from the Newton integral in 3D, that the compensation potential becomes:

$$(Vc)\_P = G \iint\_{\sigma} \Delta \rho \int\_{R-D}^{R-D\_0} \frac{r^2 dr}{l\_P} d\sigma = G \left[ \int\_{\sigma} \Delta \rho \int\_{R-D}^{R} \frac{r^2 dr}{l\_P} d\sigma + G \right] \left[ \Delta \rho \int\_{\sigma}^{R-D\_0} \int\_{R}^{r} \frac{r^2 dr}{l\_P} d\sigma,\tag{A.1} \right]$$

where the last integral term is a constant, global mean value. Disregarding this term (which does not contribute to the Moho undulation) the integral can be written in the spectral domain after integration with respect to *r* and setting *rP* ¼ *R* (sea level radius):

$$(Vc)\_P = GR^2 \sum\_{n=0}^{\infty} \frac{1}{n+3} \iiint\_{\sigma} \frac{\Delta \rho}{n+3} \left[1 - \left(1 - \frac{D}{R}\right)^{n+3}\right] P\_n(\cos \varphi) d\sigma \tag{A.2}$$

Considering the addition theorem of fully normalized spherical harmonics (Heiskanen and Moritz 1967, p. 33):

$$P\_n\left(\cos\psi\_{PQ}\right) = \frac{1}{2n+1} \sum\_{m=-n}^{n} Y\_{nm}(P)Y\_{nm}(Q),$$

one obtains

$$\delta T\_P = \bar{\mathbf{G}} \mathbf{R}^2 \sum\_{n=0}^{\infty} \frac{\mathbf{1}}{(2n+1)(n+3)} \sum\_{m=-n}^{n} Y\_{nm}(P) \left[ \left| \Delta \rho \left[ \mathbf{1} - \left( \mathbf{1} - \frac{D}{R} \right)^{n+3} \right] Y\_{nm} d\sigma. \tag{A.3} \right|$$

As *D* is small vs. *R,* one may expand the last bracket in this equation a la Taylor as

$$\frac{1}{n+3}\left[1-\left(1-\frac{D}{R}\right)^{n+3}\right] = \frac{D}{R} - \frac{n+2}{2}\left(\frac{D}{R}\right)^2 + \frac{(n+2)(n+1)}{6}\left(\frac{D}{R}\right)^3 + \dots,\tag{A.4}$$

and by inserting this series in Eq. (A.3) one obtains after integration

$$\begin{split} (Vc)\_P &= 4\pi GR \sum\_{n=0}^{\infty} \frac{1}{2n+1} \times \\ &\sum\_{m=-n}^{n} \left[ (D\Delta\rho)\_{nm} - \frac{n+2}{2R} \left( D^2 \Delta\rho \right)\_{nm} + \frac{(n+2)(n+1)}{6R^2} \left( D^3 \Delta\rho \right)\_{nm} - \dots \right] Y\_{nm}(P) \end{split} \tag{A.5}$$

where ðÞ*nm* are spherical harmonic coefficients. As the compensation potential coefficients are related to those of the compensation attraction *Ac* by

$$(Vc)\_{nm} = \frac{R}{n+1}(Ac)\_{nm},\tag{A.6}$$

one obtains the spectral equation from (A.5):

$$\frac{R}{n+1}(Ac)\_{nm} = \frac{4\pi GR}{2n+1} \left[ (D\Delta\rho)\_{nm} - \frac{n+2}{2R} \left( D^2 \Delta\rho \right)\_{nm} + \frac{(n+2)(n+1)}{6R^2} \left( D^3 \Delta\rho \right)\_{nm} - \dots \right] \tag{A.7}$$

By comparing the spectra of both sides and summing up all harmonics and considering Eq. (2),

$$D\Delta\rho = \frac{1}{4\pi} \sum\_{n=0}^{\infty} \frac{2n+1}{n+1} \sum\_{m=-n}^{n} f\_{nm} Y\_{nm} + \Delta,\tag{A.8}$$

**Author details**

**39**

(KTH), Stockholm, Sweden

West (HV), Trollhättan, Sweden

Lars Erik Sjöberg1,2\* and Majid Abrehdary2

*On Moho Determination by the Vening Meinesz-Moritz Technique*

*DOI: http://dx.doi.org/10.5772/intechopen.97449*

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

1 Division of Geodesy and Satellite Positioning, Royal Institute of Technology

2 Division of Mathematics, Computer and Surveying Engineering, University

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

where *fnm* = �*δ gB nm*/*G* and

$$\Delta = \sum\_{n=0}^{\infty} \sum\_{m=-n}^{\infty} \left[ \frac{n+2}{2R} \left( D^2 \Delta \rho \right)\_{nm} - \frac{(n+2)(n+1)}{6R^2} \left( D^3 \Delta \rho \right)\_{nm} + \dots \right] Y\_{nm} \tag{A.9}$$

accounts for higher order terms in the series.

In practical application for Moho feature determination the lower limit for the summation is found to be about 10 (as the lower harmonics are related with deep Earth gravity anomalies) and the upper limit should not exceed about 180.

*On Moho Determination by the Vening Meinesz-Moritz Technique DOI: http://dx.doi.org/10.5772/intechopen.97449*


**The E**ff**ect of a Secondary Task on Kinematics during Turning in Parkinson's Disease with Mild to Moderate Impairment**

#### **Francesca Nardello, Emanuele Bertoli, Federica Bombieri, Matteo Bertucco \* and Andrea Monte**

Department of Neurosciences, Biomedicine and Movement Sciences University of Verona, 43, 37131 Verona, Italy<EMAIL_ADDRESS>(F.N<EMAIL_ADDRESS>(E.B<EMAIL_ADDRESS>(F.B<EMAIL_ADDRESS>(A.M.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-045-8425131

Received: 10 July 2020; Accepted: 29 July 2020; Published: 3 August 2020

**Abstract:** Patients with Parkinson's disease (PD) show typical gait asymmetries. These peculiar motor impairments are exacerbated by added cognitive and/or mechanical loading. However, there is scarce literature that chains these two stimuli. The aim of this study was to investigate the combined effects of a dual task (cognitive task) and turning (mechanical task) on the spatiotemporal parameters in mild to moderate PD. Participants (nine patients with PD and nine controls (CRs)) were evaluated while walking at their self-selected pace without a secondary task (single task), and while repeating the days of the week backwards (dual task) along a straight direction and a 60◦ and 120◦ turn. As speculated, in single tasking, PD patients preferred to walk with a shorter stride length (*p* < 0.05) but similar timing parameters, compared to the CR group; in dual tasking, both groups walked slower with shorter strides. As the turn angle increased, the speed will be reduced (*p* < 0.001), whereas the ground–foot contact will become greater (*p* < 0.001) in all the participants. We showed that the combination of a simple cognitive task and a mechanical task (especially at larger angles) could represent an important training stimulus in PD at the early stages of the pathology.

**Keywords:** Parkinson's disease; gait; dual task; turning; kinematics

#### **1. Introduction**

Gait functionality is accomplished with good health, whereas locomotion disturbances are associated with an advancing age or with the presence of pathologies. Within 3 years of diagnosis, over 85% of people with Parkinson's disease (PD) develop gait deteriorations [1,2], as well as a reduced walking symmetry [2–4]. Such motor dysfunctions could be exacerbated under various conditions, such as the dual task performance and the changing of the walking direction (i.e., turning).

Specifically, the execution of the two tasks simultaneously ("concurrent performance") presents a challenge for PD patients because of their disabled lower-level spinal centers and basal nuclei [5–7]. Studies have shown that, when compared to controls, subjects with PD show greater reductions in their stride length [8,9] and, therefore, walking speed, as well as an increased variability of the kinematic parameters [10–16]. These patterns are aggravated with the severity of the pathology [17,18] and with the complexity of the concurrent task [5,13,19,20]. Since these findings strengthen the idea that the cognitive function could contribute to gait regulation, it could be helpful to understand the effect of different cognitive tasks, even very simple ones, in this kind of pathology [21–23]. This knowledge could provide an insight on what is the best training stimulus to be administered in those patients.

Concerning the walking direction, it has been shown that subjects with PD report turning difficulties, which are associated with an augmented risk of falling [24–27]. These impairments are due to the central nervous system's involvement in body re-orientation when travelling in the new direction.

From a kinematical point of view, patients with PD highlighted a smaller step width [9,28], a reduction in the step length and a decrease in their walking velocity during a change of direction of 45◦ in the stepping tasks [29]. If the turning angle increased (i.e., to 90◦), patients approached turns with a slower step length and by performing the turn with a larger number of steps [30]. Walking with an auditory cue reduced the gait-timing variability, as well as the step length, and increased the radius of gyration during a turn of 180◦ [31]. Moreover, recent literature has shown that subjects with PD spent more time during the turn (between 30◦ and 180◦) and exhibit a reduction in the walking stability compared to controls [32]. Turning 360◦ in place seemed to be a more compromised condition when compared to turning while walking, with similar impairments with and without episodes of freezing [33,34]. As in the case of the cognitive additional demand, the turning conditions could also represent an important stimulus in such a population. Hence, investigating different turning conditions could provide important information about the optimal training strategy in people with PD.

To date, a lingering question in the literature regards the combination of mechanical and cognitive perturbations. Indeed, few studies have combined the dual task condition with the mechanical perturbations (e.g., change of direction) [35–37]. Furthermore, previous studies usually used turn angles larger than 90◦, which represents per se an important perturbation for PD patients [30].

Therefore, we used the most common turning angles during daily life (60◦ and 120◦) [38], in combination with a simple cognitive task (repeating the days of the week backwards), to better understand the effects of these perturbations on gait kinematics. This combined approach has involved participants with mild to moderate PD in order to provide additional information on how the typical Parkinson's disease walking pattern is modified under simultaneous cognitive and mechanical loads.

Hence, the primary aim was to investigate if the presence of a "dual task" condition can further alter the kinematics (i.e., spatiotemporal parameters) of walking, or, on the contrary, if it can represent a good training stimulus in mild and moderate Parkinson's disease. This cognitive task was studied during forward/linear walking and during turning.

Based on the previously mentioned literature, we hypothesized that the simple cognitive and the mechanical tasks will exhibit similar effects in both populations (patients and controls), whereas the combination of the two stimuli will show a higher impact on PD patients.

#### **2. Materials and Methods**

#### *2.1. Participants*

This study has a cross-sectional, analytical, observational design. Nine patients with mild to moderate PD (3 women and 6 men; mean ± SD, 68.2 ± 5.95 years old, 74.2 ± 11.8 kg body mass, 1.70 ± 0.10 m height, and 25.6 ± 3.07 kg·m−2 body mass index (BMI)) and nine healthy age-matched controls (4 women and 5 men; 67.2 ± 3.45 years old, 72.6 ± 13.1 kg, 1.68 ± 0.08 m, and 25.7 ± 3.43 kg·m−2) were recruited in this study.

All participants received written and oral instructions before the study and gave their written informed consent for the experimental procedure. The study was conducted in accordance with the Declaration of Helsinki, and the experimental protocol was approved by the Institutional Review Board of the Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona (protocol number 2018-UNVRCLE-0451799).

Participants were recruited from a sample of late adulthood people attending the adapted physical activity program at the School of Sport and Exercise Science at our University (see their characteristics in Table 1).

 **1.**Characteristics of subjects with Parkinson's disease.

**Table**

Patients were excluded if their medical condition proved unstable due to other neurological, orthopedic, metabolic or cardiovascular co-morbidity factors affecting gait. There was no type of rehabilitation in the month prior to recruitment or disease-modifying therapy that was not well defined. A diagnosis of idiopathic PD was carried out by a neurologist, in accordance with the guidelines established by the London Brain Bank [39]. The disease severity was classified according to the modified Hoehn and Yahr scale (H&Y) [40], whereas the assessment of the degree of motor and functional impairment was obtained using part III of the Unified Parkinson's Disease Rating Scale (UPDRS) [41]. Finally, cognitive function was assessed by using the Mini Mental State Examination (MMSE), with scores of up to 30 (higher scores correspond to greater cognitive function).

We conducted an a priori screening to evaluate whether participants were able to carry out the task instructions required for the experimental protocol (see the "Experimental procedures" later). Based on this a priori screening, only individuals with PD with an MMSE score of 24 or higher were able to perform the double stimuli, and therefore were recruited in the study.

#### *2.2. Experimental Procedures*

Tests were conducted in the Biomechanics Laboratory at the Department of Neurosciences, Biomedicine and Movement Sciences. The spatial (distance) and temporal (time) characteristics of the step pattern were measured using an eight-camera motion capture system (MX Ultranet, VICON, Oxfordshire, UK), sampling at 100 Hz. This apparatus recorded the position of markers positioned bilaterally on the feet: the calcaneus, lateral malleolus and 5th metatarsal-phalangeal joint.

Participants started from a standing position and walked barefoot at their self-selected speed over a 10 m walkway (Figure 1, panel a). Each participant performed three different conditions: *(i)* forward walking (WF (Figure 1, panel b)); *(ii)* walking, turning at 60◦, walking (T60); *(iii)* walking, turning at 120◦, walking (T120). Furthermore, T60 and T120 were conducted turning in both directions (left and right (Figure 1, panels c and d)). During all turning tasks, a cone was positioned in the center of the walkway (see the black hexagon in Figure 1) to identify the turning point. Furthermore, an operator positioned himself at the end of the corridor lane that the subjects had to move. All directions were tested in single and dual tasks, and the cognitive load consists of walking while repeating the days of the week backwards [17]. Six trials of each condition were performed and the order of gait conditions was randomly allocated (Figure 2). Subjects sat and rested in a chair for three minutes between trials. Patients were tested at the peak dose in the medication cycle.

**Figure 1.** Schematic representation of walking conditions (**a**), and steps during walking (forward walking (WF), (**b**) and turning (T60 in (**c**) and T120 in (**d**)).

**Figure 2.** Flow chart of the research design.

#### *2.3. Data Reduction*

Kinematic data were recorded from 3 m before and after the turning step. The quantitative gait assessment included both temporal and spatial parameters (Figure 3), that are: *(i)* stance time (s), the period of time when the foot is in contact with the ground; *(ii)* swing time (s), the period of time when the foot is not in contact with the ground; *(iii)* stride time (s), the interval of time to complete a gait stride; *(iv)* cadence (stride·min−1), the number of strides taken in a unit of time; *(v)* stride length (m), the distance from the initial contact of one foot to the following initial contact of the same foot. All these parameters were obtained using standard definitions according to an algorithm programmed in LabView (version 10, National Instruments, Austin, TX, USA). Walking speed (m·s−1) was appreciated as the distance travelled during a complete stride cycle.

**Figure 3.** Gait parameter assessment for walking/turning conditions.

The average value of all the performed steps in each condition has been utilized in further analyses. The number of errors (i.e., when telling a day backwards went wrong) was counted and collected by an operator during the walking trials as well. For controls, we pooled together the data from the right and left lower limbs (*n* = 9), whereas for PD patients, the data referring to the less affected side (PDNA) and to the more affected side (PDA) were considered separately (*n* = 9).

Finally, we calculated the locomotor rehabilitation index "LRI" as: LRI = 100 \* (self-selected speed (SSWS)/optimal speed (OWS)), where the self-selected speed (SSWS) has been directly measured and the optimal speed (OWS) has been estimated according to a previous study [42].

#### *2.4. Data Analysis*

The data analysis was conducted using SPSS 19.0 (SPSS Inc, Chicago, IL, USA). Descriptive statistics were used to compute the means and standard deviations for the outcome variables. All the spatiotemporal gait data were normally distributed (Kolmogorov–Smirnov and Shapiro–Wilk tests) and did not violate the assumptions of homogeneity. In order to test the main hypotheses, a series of 2 (tasks: single task vs. dual task) × 3 (gait directions: WF vs. T60 vs. T120) repeated measures ANOVAs, with groups (CR, PDNA, PDA) as the between factors, were used to analyze the gait data. When a significant main effect was found (critical *p* value < 0.05), a post-hoc *t*-test was performed. A Bonferroni correction was applied when needed.

#### **3. Results**

#### *3.1. Cognitive Task*

For the MMSE, controls (28.1 ± 1.66) showed similar scores to patients (27.2 ± 1.85 (p = ns)).

The majority of the dual task trails were successful. Whereas individuals with PD performed the cognitive task with 97.2 ± 6%, 94.9 ± 8% and 95.2 ± 10% accuracy during WF, T60 and T120, respectively, and controls performed the same trails with an accuracy of 96.7 ± 5%, 95.3 ± 6% and 94.9 ± 9%. Regarding the gait direction, more correct answers were given while walking forward as compared to turning (*p* < 0.01 for T60 and T120) similarly in both groups.

#### *3.2. Spatiotemporal Parameters*

There were no differences between groups for age, body mass and height, or body mass index.

The average ± SD values of the measured temporal and spatial variables have been reported in Tables 2 and 3, as well as the post-hoc results.

The stance time differed among tasks (F (1, 24) = 34.456, *p* < 0.001) and walking direction (F (2, 48) = 27.234, *p* < 0.001), whereas there were no differences between groups (F (2, 24) = 0.262, p = ns). In particular, the dual task showed an increased stance time compared to the single one. The highest stance phase was observed during turning at 120◦, while the lowest was during walking in the straight direction.

The swing phase showed a significant effect for direction (F (2, 48) = 4.648, *p* < 0.05), but not for task (F (1, 24) = 3.745, p = ns) and group (F (2, 24) = 0.883, p = ns). Particularly, the 120◦ turn took a longer swing time in comparison to the other directions.

The cycle time showed significant differences among tasks (F (1, 24) = 31.840, *p* < 0.001) and directions (F (2, 48) = 34.227, *p* < 0.001), whereas there were no group changes (F (2, 24) = 0.618, p = ns). Both groups increased the cycle time during the dual task condition, and the 120◦ turn took the longest time compared with the other conditions.

Therefore, the step frequency and cadence changed significantly with regard to task (F (1, 24) = 30.257 and 30.317, *p* < 0.001) and direction (F (2, 48) = 31.329 and 31.441, *p* < 0.001), but not with group (F (2, 24) = 0.458 and 0.457, p = ns).

0.88 ± 0.09 b,g,m

> T120\_DT

CR: control group; PDNA: Parkinson's disease not affected side; PDA: Parkinson's disease more affected side. WF: walking forward; T60: turning at 60◦; T120: turning at 120◦. ST: single

task; DT: dual task. a = *p* < 0.05, b = *p* < 0.01, c = *p* < 0.001: significant difference by comparing ST to DT. d = *p* < 0.05, e = *p* < 0.01, f = *p* < 0.001: significant difference by comparing the

WF to T60. g = *p* < 0.05; h = *p* < 0.01, i = *p* < 0.001: significant difference by comparing the T60 to T120. m = *p* < 0.01, n = *p* < 0.001: significant difference by comparing the WF to T120.


*2.3. Cell Viability Assay and E*ffi*cacy Study of dec-RVKR-cmk*

The cytotoxic concentration 50 (CC50) of dec-RVKR-cmk was determined using the CellTiter-GLO One Solution Assay kit (Promega). This assay was used to detect the viability of cultured cells on the basis of ATP quantification of cells. Briefly, Vero and C6/36 cells were seeded (10,000 cells per well) in a 96-well plate, 24 h before compound treatment. Culture supernatants were replaced with different concentrations of dec-RVKR-cmk or DMSO. Each concentration was tested in triplicate. After 72 h, cells were washed with phosphate-buffered saline (PBS) and an equal volume of (100 μL) CellTiter-GLO reagent was added to each well. For appropriate cell lysis, cells were agitated in a shaker for 2 min and then incubated for 10 min at room temperature. A multimode plate reader was used to quantitate luminescence signals in each condition and then the luminescence value was compared with its corresponding DMSO control. The efficacy of dec-RVKR-cmk against ZIKV (0.2 MOI) and JEV (0.2 MOI) was studied by using different concentrations (1, 10, 50, and 100 μM). The inhibitory concentration 50 (IC50) of dec-RVKR-cmk was determined by counting visible plaques produced by ZIKV or JEV. Both CC50 and IC50 were calculated by non-linear regression model using GraphPad prism7.


**5. Marinobufagenin and CV Diseases**

Most of the studies designed to evaluate the pathogenetic mechanisms by which MBG contributes to CV disease risk have been performed in animal models. Increased concentrations of circulating MBG (as a result of sodium loading or via infusion pump) caused several effects: vascular [24,59] and microcirculation [103] alterations, pressor changes [4,46,104,105] and cardiac and renal [8,9,58,61,91] fibrosis. Investigations in humans have shown elevated MBG plasma levels in many pathological conditions: heart failure [8], acute myocardial infarction (elevated urinary MBG levels) [106], primary aldosteronism [107], renal ischemia [108] and CKD [100,109].

There are several scientific pieces of evidence supporting a possible pathogenic role of bufadienolides in hypertensive conditions associated with hydro-saline retention. Firstly, increased plasma and urinary concentration levels of MBG were observed in volume expansion conditions and in hypertensive patients mediated by volume expansion, due to salt accumulation [46,104,105,110]. Secondly, the administration of bufadienolides in experimental animals causes hypertension [11,13,111]. Thirdly, in rats, the hypertension caused by the administration of deoxycorticosterone acetate and salt is reversed through the intraperitoneal injection of an MBG antagonist, resibufogenin (RBG) [12,13,112], which differs from MBG only in the absence of a hydroxyl group in the β 5 position. Finally, the use of anti-MBG antibodies in salt-loaded pregnant rats and in salt-sensitive hypertensive rats, results in the reduction in blood pressure values [113].

In recent years, several evaluations of whether MBG could be used as an early marker of CV risk have been performed. The first study designed to evaluate the possible asso-

ciation between blood pressure values and MBG plasma levels demonstrated an inverse relationship between diastolic blood pressure values and the urinary excretion (24 h) levels of MBG in the case of high sodium intake (16.32 g of salt per day). The natriuretic effects of MBG could represent a homeostatic mechanism to restore blood pressure values to normal, constituting a protective mechanism in healthy subjects [82]. The dietary intervention had a total duration of 12 days; therefore, that effect could represent a homeostatic mechanism in the short term. In contrast, another group has shown that MBG plasma levels are positively associated with systolic blood pressure values during the period of high sodium intake (5 weeks). In that case, it might reflect a long-term homeostatic response in which the vasoconstrictor activity of MBG could superimpose the natriuretic effect [114]. Fedorova et al. showed completely different responses from the above studies. In a cohort of men and women, there was an increase in systolic blood pressure values following dietary sodium loading without changes in both plasma and urinary MBG concentrations [81]. Therefore, the results on the relationship between blood pressure values and circulating and urinary MBG levels in humans are conflicting and require further evaluation.

Recent findings have revealed that in a state of inactivity, sodium can settle in the interstitium between the skin and organs [115]. The alteration of these deposits could also affect blood pressure values. We can say that high sodium intake correlates both with a higher production of MBG and with rigid large arteries, even in healthy subjects. These pieces of evidence were confirmed in laboratory models, as risk factors for dementia and CV events [17]. In addition to MBG, several factors act on peripheral vascular resistance, such as neurohormonal, baroreflexes and myogenic factors [49]. It is also known that sodium regulates the rigidity of endothelial cells and modifies the release of nitric oxide by altering the tone of blood vessels and blood pressure [116]. In the current state of the art, there is still not enough evidence to consider urinary MBG excretion as a predictive value of increased cardiovascular risk before the onset of the disease [24], although numerous studies have confirmed the correlation between plasma MBG levels, sodium and occurrence of arterial hypertension. Furthermore, these results proved to be more applicable to men than to women.

In other parts of the body, such as the arterial vascular smooth muscle cells, MBG generates an increase in cytosolic Ca2+ amount that causes vasoconstriction through the activation of an "ionic pathway" [4]. In addition to this "ionic pathway", MBG can activate different intracellular signaling pathways that trigger cellular effects, such as cell proliferation, ROS genesis or the stimulation of apoptosis, also via the activation of pathways with other molecules, such as Phospholipase C-*γ* isozyme (PLC-γ), Phosphatidylinositol 3-kinase (PI-3K), IP3 receptor type 3 (IP3R), and ankyrin [117].

Specifically, it has been reported that cardiotonic steroids activate a signal cascade, which is mediated through Src, Ras, ROS, and ERKs, and promote endocytosis of the plasmalemmal Na+/K+-ATPase [10,99,118–120]. The activation of this cascade requires that the Na+/K+-ATPase to be in caveolae in order to proceed [121,122]. This series of signals is known to cause changes in gene expression, which can be inhibited by antioxidant molecules [7,10,120].

Arterial stiffness is well known to be related to increased CV risk and death in individuals of all ages [123], regardless of blood pressure values. Sodium intake also correlates with arterial stiffness regardless of hypertensive status [124,125], even in healthy subjects. Thus, a possible association between MBG and arterial stiffening was hypothesized. Jablonski et al. have demonstrated, in individuals with high or hypertensive blood pressures, that a positive association between MBG and carotid to femoral pulse wave velocity (cf-PWV) [114] is actually the gold standard measurement of large artery stiffness [126]. The same positive correlation has also been demonstrated in young healthy women, regardless of salt intake [80]. To date, the pathogenetic mechanisms through which MBG is able to determine arterial stiffness are unknown, although MBG has already been shown to promote the development of vascular fibrosis in the aorta of rats [59]. The mechanism by which MBG promotes collagen production and deposition was studied in cultured rat

smooth muscle cells and was always dependent on the inhibition of Na+K+-ATPase [58,59]. Collagen-1 production is secondary to the marked downregulation of transcription factor Friend leukemia integration-1 (Fli-1) [58,59]. In fact, it has been shown that MBG could also sub-regulate a negative regulator of collagen-1 synthesis, Fli-1. The phosphorylation of Fli-1 through the active form of Protein Kinase C Delta (PKC-d) induces the activation of a collagen gene promoter. In vitro, MBG was found to be an activator of the Fli-1 pathway in cultured fibroblasts and smooth rat muscle cells.

Another important predictor of both increased CV risk and mortality is the left ventricular mass (LVM) measured using the echocardiogram [127]. In the CARDIA study (Coronary Artery Risk Development in Young Adults), a possible positive association between LVM and sodium in 24h urine levels was shown in young adults, although this relationship was confounded by the presence of obesity [128]. From this observation, it was hypothesized that the increase in MBG plasma levels, induced by sodium intake, may be associated with an increase in LVM. Strauss et al. have observed a significant association between LVM and MBG in young adults: in this case, the relationship was independent on obesity and also on blood pressure values, suggesting a possible pathway through which MBG induces myocardial hypertrophy [25].

To further confirm the correlation between MBG plasma levels and fibrosis, it has been shown that rats with (experimental) renal impairment have increased MBG plasma values, along with cardiac and renal fibrosis. In these mouse models, MBG promotes procollagen-1 expression by cultured cardiac fibroblasts. As procollagen expression increases, collagen and procollagen-1 mRNA increase too. Considering this, MBG probably induces a direct increase in collagen expression by fibroblasts [9]. It has also been observed that in spontaneously hypertensive (SHR) and normotensive Wistar–Kyoto (WKY) mouse models, following a diet rich in sodium, hypertension and left and renal ventricular hypertrophy, due to fibrosis with the overexpression of TGF-β1 mRNA, were recorded. This has resulted, in both glomerular and peritubular sites, in an increase in collagen type 1 [129]. Interestingly, in animal models characterized by prolonged salt intake, diet-related profibrotic effects were eliminated through treatment with anti-MBG antibodies, without generating hemodynamic effects [130]. The administration of specific antibodies against MBG reduced aortic fibrosis and favored relaxation at the level of aortic fibers. The differences that can be attributed to the vascular component without hemodynamic changes, indicate that the possible vascular stiffening is independent of blood pressure and that the profibrotic factor generated by MBG is responsible for it [131].

Even in laboratory models treated with antibodies to MBG and previously subjected to a diet high in sodium, there was a reduction in systolic blood pressure and also a reduction in the weight of both the heart and kidneys. In these mouse models, TGF-β expression, which had previously been increased, was also downregulated after treatment with anti-MBG antibodies. The immunoneutralization of MBG resulted in the downregulation of genes involved in profibrotic expression. In addition, the normalization of renal function through creatinine clearance was also observed, probably due to the reduction in renal fibrosis, as it was accompanied by a significant decrease in kidney weight for the reduction in type I-III-IV collagen amounts. In mouse models, it has also been observed that treatment with anti-MBG antibodies reduces the development of heart failure. This was established by estimating ventricular weight via ultrasound in hypertensive rats immunoneutralized for MBG, compared with non-immunoneutralized hypertensive rats.

Early vascular damage was also reduced after immunoneutralization. In these models, a reduction in the mRNA expression of TGF-β1, FN1, MAPK1, Col1a2, Col3a1 and Col4a1 was also noted compared with those not treated with antibodies of MBG. This demonstrated how MBG initiates TGFβ-1-signaling in cultured ventricular myocytes, through Na+/K+-ATPase signal transduction and other factors, such as tissue angiotensin II [132]. In humans, sodium restriction also reduces urinary MBG production and excretion, resulting in reduced blood pressure and aortic stiffness. Another factor may impact fibrosis, sodium

concentration and MBG plasma levels: it has been observed that the sensitivity of blood pressure dietary salt intake increases with increasing age [23,133,134].


*2.1.4 Radon chamber*

A radon chamber is a container that houses a radon atmosphere for calibration of radon detectors and conduction of studies in the field of radon metrology. It consists of tools for relative humidity, pressure, and temperature, which must be at a constant range for calibration of radon detectors. **Table 1** summarizes the type, size, and accommodation capacity of radon chambers; furthermore, the radon generating source must be traceable to an international or national standard.


*4.2. Time and Memory Complexity*

As mentioned above, inverting the full *cs* × *cs* dimensional covariance matrix to construct STBF-EMP and STBF-SHRUNK can be costly and unstable, in particular in highresolution settings with many EEG channels or time samples. Constructing the full covariance and inverse covariance matrices also requires a considerable amount of memory. The structured covariance estimator of STBF-STRUCT has two advantages here.

First, because of Properties 1 and 2 there is no need to calculate the full *cs* × *cs* symmetric covariance and inverse covariance matrices for STBF-STRUCT or keep them in memory; they can instead be replaced by two smaller symmetric matrices of dimensions *c* × *c* and *s* × *s*, respectively. Furthermore, since the temporal component of the Kronecker product is Toeplitz-structured, it only requires *s* parameters to estimate. Although the inverse covariance of STBF-EMP and STBF-SHRUNK is defined by *cs*(*cs*+1) 2 = 32×17(32×17+1) 2 = 122.128 parameters accounting for the symmetric nature of covariance, the structured estimator only requires *c*(*c*+1) 2 + *s* = 32(32+1) 2 + 17 = 545 unique parameters. This reduction in parameters to estimate reduces memory usage and contributes to the regularization effect for low-data-availability settings. The inverse covariances of STBF-EMP and STBF-STRUCT, represented as 32 × 17 × 32 × 17 symmetric matrices of single-precision real floating point numbers for weight calculation, use 9.03 MiB of memory. The 32 × 32 and 17 × 17 matrices of STBF-STRUCT only require 5.12 KiB.

Second, structured estimation has better time complexity. Covariance estimation and inversion occupy the largest part of the STBF training time. For STBF-EMP and STBF-SHRUNK, the time complexity of this process is O(*nc*2*s*2 + *c*3*s*3). Thanks to Property 1, the complexity can be reduced to O(*nc*2*s*2 + *c*3 + *s*3) for the structured estimator of STBF-STRUCT. The results presented in Figure 4 confirm these calculations. It can be observed that the training time of STBF-STRUCT stays low compared to STBF-EMP and STBF-SHRUNK when dimensionality increases.

The results shown in Figure 4 also confirm that the STBF-based estimators are very fast to train compared to the state-of-the-art estimator xDAWN+RG, which confirms the results in [21]. Since the training times of all STBF-based classifiers are already of the order of tenths of seconds, the question arises as to whether the improvements achieved using the structured estimator would be relevant in practice. However, the authors believe that these results could significantly impact some use-cases of the spatiotemporal beamformer, such as high-spatial- or temporal-resolution ERP analyses. One example is single-trial ERP analysis with a high temporal resolution to extract ERP time features. Such higher-resolution analyses can later be incorporated into an ERP classification framework. In addition, the speed provided by structured estimation yields a faster off-line evaluation of the STBF ERP classifier, in which multiple cross-validation folds, subjects, and hyperparameter settings often need to be explored, which can quickly increase runtime. Improvements in computation speed and memory usage can remove the need for dedicated computation hardware and enable group analyses to be run on a personal computer.

#### *4.3. Interpreting the Weights*

The weight matrix of the STBF determines how each spatiotemporal feature of a given epoch should contribute to enhancing the SNR of the discriminating signal in the classification task. Alternatively, the activation pattern can be regarded as a forward EEG model of the activity, generating the discriminating signal and the weights as a backward model [60,64]. Regularization enables a researcher to interpret better the distribution of the weight over space and time after reshaping the weight vector **w** to its spatiotemporal matrix equivalent, *W*, such that vec(*W*) = **w**. Figure 5 compares the weights calculated in STBF-EMP and STBF-SHRUNK with the weights from STBF-STRUCT.

**Figure 5.** Spatiotemporal beamformer weights calculated using four blocks of data (of 1215 epochs) from *Subject 01* from 0.2 s before until 1.0 s after stimulus onset. Regularized weights show an interpretable sparse pattern, whereas the empirical weights appear noisier. (**A**) Spatiotemporal activation pattern with spatial and temporal global field power. (**B**) STBF-STRUCT weights with spatial and temporal averages of absolute values. (**C**) STBF-SHRUNK weights. The shrinkage factor *α* = 0.05 was determined with the closed-form LOOCV method. (**D**) STBF-EMP weights.

Since the linear filter's noise suppression and signal amplification functions are deeply entangled, it is not necessarily true that features with a high filter weight directly correlate to features containing discriminatory information [64]. However, it is still possible to interpret the weights in terms of which features contribute most to the classification process, be it through noise suppression, signal amplification, or—most likely—a combination of both. The weights obtained by STBF-EMP seem to be randomly distributed over space and time; the regularized estimator used by STBF-SHRUNK and STBF-STRUCT reveal a more interpretable weight distribution. The STBF-SHRUNK weights show a sparse spatial distribution, whereas the STBF-STRUCT weights show a sparse distribution in both space and in time.

As expected, Figure 5b and d exhibit weight around the central and parietal regions, where the P300 ERP component is present. Especially the spatial weights of STBF-SHRUNK in Figure 5d correspond to the spatial activation pattern in Figure 5a. This is not surprising, since shrinkage transforms the covariance matrix closer to the identity matrix and assuming identity covariance in Figure 4 yields weights identical to the activation pattern (up to a scaling factor). Additionally, Figure 5b shows that weights in the baseline interval and after 0.6 s, which should contain no response information, are close to zero for the structured

estimator. Meanwhile, these weights are high in the occipital region between 0.1 s and 0.2 s, containing early visual components with relatively low SNR. This high weight for the early visual components confirms the results from Treder and Blankertz [65] that state that, in addition to the P300, the early N1 and P2 ERP components are also modulated by oddball attention and contain discriminatory information between attended and nonattended stimuli.

Using an interpretable classification model has many advantages. For instance, one can use the weight matrix to determine relevant time samples and EEG channels for persubject feature selection to refine the model further. The number of channels is also an important cost factor in practical BCI applications. Determining which channels do not contribute to the classification accuracy helps to reduce the number of required electrodes. Spatially clustered weights indicate that some electrodes are not used by the classifier and can be discarded accordingly with no substantial accuracy reduction. As another example, information about the timing and spatial distribution of the discriminatory information in the response can be extracted from the weights and linked to neurophysiological hypotheses.


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6.1. External and internal fouling

The classification of the fouling phenomenon depends on the location of the accumulated rejected salts; it can be viewed as [40, 41]:

EF involves accumulation of rejected salts on the surface of the membranes by three distinct paths:

Typically, the three mechanisms can occur in any combination at any given time. However, external membrane fouling of ROMs is most frequently caused by biofouling.

IF is a regular loss of membrane productivity due to changes in its chemical structure either by physical compaction or by chemical degradation. Physical deterioration of the membrane may result from long-term application of feed stream at pressures higher than that designed for the ROMs; they are designed to handle 83 bars for sea water reverse osmosis membranes and/or by their continued setup at source water temperatures above 45C, the limit of safe membrane operation. Chemical deterioration results from continuous exposure to strong oxidants, e.g., chlorines, bromines, ozones, permanganates, peroxides chemicals, and very strong acids, typically pH < 3 and alkali at pH > 12.

leads to critical negative significances on the ROM function. They include increased osmotic pressure, increased salt extract, creation of hydraulic opposition of water stream, and Induc-

Concentration polarization cannot be evaded; it can only be reduced before taking any corrective measures; concentration polarization should be quantified. This quantification occurs in three separate consecutive paths. They can be emphasized as balancing the chemical and mass balance equations across the boundary layer, balancing the transport equations over the ROM and determination of solute transport equations within the pores of the ROM. System performance can be predicted by simultaneous solution of all these three equations. Based on the type of concentration polarization, there are two classes of models: an osmotic pressure-

In this situation, solute particles form a viscous boundary layer concluded on the surfaces of ROMs [45–47]. Solute concentration increases from the bulk to membrane surface concentration across the mass transfer barrier layer. In this case, the width of the mass transfer boundary layer is constant. At any cross section of the boundary layer for the concentration gradient, dc

/s.

dy ¼ 0 (2)

.s; c and cp are the bulk and permeate concentrations

Recent Drifts in pH-Sensitive Reverse Osmosis http://dx.doi.org/10.5772/intechopen.75897 13

vw c � vw cp þ D dc

/m2

dy,

tion of scale and fouling on the ROM.

controlled model and a gel layer-controlling model.

Figure 6. Boundary layers in a membrane-feed spacer. RO, reverse osmosis.

6.3. Osmotic pressure controlled model [OPCM]

at the steady state, the solute mass steadiness leads to

; and D is the solute diffusivity in m2

where vw is the permeate flux in m3

in kg/m3

The difference between EF and IF is somewhat clear; EF could be completely reversed by chemical cleaning, while IF causes permanent damage of the micropores, resulting in an irreversible changes.


*2.2. Volume Fraction*

The Ansys-Fluent 14.5 commercial software was used in the study. The volume fraction represents the volume percentage of each phase, and each phase satisfies the law of conservation of mass and momentum. The volume fraction of the q phase (*V*q) is defined as

$$V\_{\mathbb{Q}} = \int\_{V} a\_{\mathbb{Q}}dV\tag{1}$$

where n q=1 αq = 1.

The effective density of the q phase is

$$
\widehat{\rho}\_{\mathbf{q}} = a\_{\mathbf{q}} \rho\_{\mathbf{q}} \tag{2}
$$

where ρq is the physical density of the q phase.


*4.1. Mulching, Terracing and Trees More Popular Than No Tillage*

*4.1. Mulching, Terracing and Trees More Popular than no Tillage*  By 2012, there was a large difference in the use of terraces between project (60%) and control fields (25%) (Figure 3). Terraces can greatly enhance soil moisture recharge [51] but require major initial labour inputs [52]. Cover crop uptake among project farmers increased until 2011, but declined to the initial level in 2012, possibly due to crop rotation patterns or to dissatisfaction with the practice itself (Figure 3). Cover crops/green manure grown for six weeks (planted two weeks earlier than maize), and then incorporated into soil can significantly improve soil fertility by adding nitrogen, increasing maize yield in the same season [53]. However, convincing farmers to grow cover crops By 2012, there was a large difference in the use of terraces between project (60%) and control fields (25%) (Figure 3). Terraces can greatly enhance soil moisture recharge [51] but require major initial labour inputs [52]. Cover crop uptake among project farmers increased until 2011, but declined to the initial level in 2012, possibly due to crop rotation patterns or to dissatisfaction with the practice itself (Figure 3). Cover crops/green manure grown for six weeks (planted two weeks earlier than maize), and then incorporated into soil can significantly improve soil fertility by adding nitrogen, increasing maize yield in the same season [53]. However, convincing farmers to grow cover crops mainly for soil fertility reasons was apparently difficult in the KACP (Figure 3), as in other projects [54]. Different SALM practices had different degrees of uptake, for example, mulching and terracing became popular, while no tillage did not. Minimum tillage practices have varying and sometimes negative or contradictory outcomes for smallholders in the region [55,56] and the willingness to use minimum tillage is lower than, for example, mulching [57]. It is also debatable whether minimum tillage options can act as a carbon sink [58,59].

Advice on SALM practices to be used, and practices to be avoided, seemed to be adhered to more by project farmers in 2012 compared with the initial use in 2009, and also compared with control farmers and with the uptake of practices in a similar study [60]. Use of soil fertility management practices is generally low among smallholders in Western Kenya [60] but is influenced positively by, for example, plot size, market and labour access, off-farm earnings and knowledge [60,61]. This implies that the poorest farmers may have less possibility to use such practices.

Trees in the agricultural landscape, that is agroforestry, can have both positive and negative effects on production and other ecosystem services, but most studies show a net positive effect [62]. Fodder trees were promoted by the KACP advisors, which can explain why project farms had a larger share of leguminous fodder trees than control farms (Figure 4). A lack of fodder trees on control farms could also explain the lower use of crop residues for mulch, as more crop residues might be needed for livestock feed. The larger number of trees per farm in Bungoma than Kisumu may be because the farms were larger in Bungoma. It is also easier for tree seedlings to survive in Bungoma since most farmers control their grazing animals and prevent them from browsing on tree seedlings.

#### *4.2. Terraces and Fodder Trees Increased Maize Productivity*

Of the six SALM practices and three non-SALM practices analysed, only terraces had a significant positive effect on maize yield. The differences in SALM practices between 2009 and 2010 (when the yield increase was largest for project farms) were mainly the increased use of cover crops and water harvesting structures. However, neither practice showed any significant correlation with yield. The four-year study period can be expected to be sufficient to reveal the effects of most management practices [63]. While the main aim of SALM practices in the KACP is to reduce greenhouse gas emissions and sequester more carbon, they can also improve water availability for crops and soil fertility, both of which improve yield [17,64]. However, few SALM practices showed any significant effects on maize yield in this study. One drawback related to terraces is the relatively high labour demand needed upon establishment, which may restrict farmers from using the measure.

The intensity of the different SALM practices was not studied (since data was not available), but differences in intensity could have masked relationships between management and yield. Most recommended practices are dependent on the type, quantity and quality of, for example, mulch, compost, water harvesting or cover crops. Project farmers, for example, reduced burning and removal of residues (for fodder or fuel) in order to use more as mulch, but 40% of residues were still used for purposes other than mulching in 2012 (Figure 3), suggesting that much is used as fodder and could be returned as manure. Mulching is known to increase and stabilise maize yield [55,57]. However, practices related to crop residues (mulching, composting, green manure, etc.) require trade-offs between several uses, and the end-use often reflects more immediate concerns such as feeding livestock rather than improving soil fertility in the longer term [65,66]. With more fodder trees on the farm, feed and fuel could come from sources other than crop residues.

The use of agroforestry SALM practices was popular in project farms, which had on average around double the number of trees as control farms. A possible drawback with tree–maize intercropping is competition for light, water and nutrients unless the trees are adequately pruned above and below ground [67]. However, the number and type of trees did not have any effect on the first-season maize yield. Timber trees, which were the most common type used, are often planted in woodlots and therefore rarely interfere with crops, while fodder trees are more often intercropped within fields. In this study, second-season maize yield was positively affected by the total number of trees, possibly because farms with many trees had a larger share of fodder trees that are leguminous and can fix the nitrogen from the atmosphere, which has been found earlier [67,68]. Apart from increasing second-season maize yield, trees could provide other services (animal feed, fruits, timber and firewood) which were not considered in this study. Further, a combination of trees and grass is effective in holding and maintaining the soil on hillslopes and terraces [69]. Agroforestry is known to be both knowledge and labour intensive which can limit the uptake of the practice [70].

#### *4.3. Maize Productivity Increased but SALM Practices Only Part of the Explanation*

Average maize yield on small, subsistence-focused farms in western Kenya was 0.9 tonnes ha−1 during the study period [71]. Nation-wide, mean annual maize yield in Kenya in the four years 2009–2012 was 1294, 1725, 1584 and 1737 kg ha−1 [72], compared with 1707, 2139, 2669 and 2921 kg ha−1 for both seasons and all farmers in this study. The first cropping season usually has more rain days and larger amounts of rainfall ("long rains") than the second season ("short rains") in both study areas, which largely explains the higher productivity in first-season maize (Figures 5 and A1). Farmers who composted their manure applied it to fields once per year at the beginning of the first season, which may be another factor in the yield differences between seasons. Overall, Bungoma had higher amounts of rainfall and a cooler climate (Table A2 and Figure A1), leaving more water available for crops instead of being lost through evapotranspiration. Therefore, productivity was significantly higher in Bungoma and the pattern was the same for all years and seasons (Figure 5).

Water is not the only limiting factor for maize growth in Western Kenya, as nitrogen and phosphorus are also main limiting factors [64]. Small-scale farms with subsistence-focused production tend to have resource flows showing net nutrient export and low values of carbon and nitrogen especially [71]. Smallholders in the KACP were encouraged to practise integrated soil fertility management, with careful use of inorganic fertiliser and more focus on organic fertilisers. This advice is debatable since many researchers view inorganic fertilisers as important in increasing yield and boosting biomass production for higher carbon cycling [73,74]. The use of inorganic fertilisers may partly explain the different yield patterns for project and control farms (Figure 6a). However, the use of inorganic fertilisers was not included in this study and therefore needs further studies for a better understanding of its effects with or without SALM practices.

The reason why the first-season yield increased more than second-season yield in 2012 (Figure 6b) was likely that yield actually decreased in 2010 for control farms, and also that SALM use was higher for first-season crops and high for both project and control farmers in 2012. The greater yield increase in Bungoma than Kisumu for first-season crops (Figure 6c) could be because Bungoma farmers concentrate on agriculture, while Kisumu farmers were traditionally fishermen and also have more off-farm job opportunities close by, making them less dependent on farming [66]. Farmers who are more focused on farming are likely to be more eager to invest in their farm and use new practices. The Bungoma area (known as "Kenya's breadbasket") also has more responsive soils and a more favourable climate than the Kisumu area.

#### *4.4. Role of Control Farms in Interpreting Results*

Immediate effects from SALM practices related to water-holding capacity [64,75] could explain the initial higher yield increases for project farms. Possible reasons why the control farms increased their yield more than project farms in 2011–2012 (Figure 6a) could be reductions in inorganic fertiliser use in project farms, control farms copying practices from project farms or project farmers paying more attention to their crops during the first years of the project, but then relaxing. Learning from neighbours and friends (farmer-to-farmer) is often the most common [76] and most effective [77] way of learning new practices in this region, which could explain both the relatively high SALM uptake in 2012 and delayed yield increases on control farms compared with project farms. Farmer-to-farmer uptake within the KACP areas was reported by Hughes et al. [42], who found significant uptake of certain practices within the project area (not only among the targeted farmer groups) than in neighbouring areas outside the project. However, this does not mean that advisory services were not worth the effort. Farmers generally want better access to advisory services [78], especially in the early stages of adoption [77].

The project and control farmers were all within the KACP areas and lived in the same villages. For control farms, the use of the different SALM practices was only studied in 2012 and by then their use of practices recommended by the KACP was on average higher than the initial (2009) use on project farms. Maize productivity at the start of the project (2009) was higher on project farms than control farms and, while maize yield increased over the years, the difference between project and control farms

was similar in 2012. Thus, comparisons of maize yield initially and at the end of the monitoring period showed no overall difference between project and control farms. The value of having control farms in addition to the baseline production in 2009 was to ensure that the yield differences were not normal annual fluctuations due to weather patterns. Control farms were two or three farms away from their paired project farm, so soils and weather conditions were probably similar. However, there might be other differences between project and control farms.

Participation in the KACP was voluntary and participants were generally both asset-poor and income-poor. However, earlier research has found higher adoption of SALM practices among the less poor in the project [41]. This may indicate that it is not the poorest of the poor that risk venturing into such projects, as has been found elsewhere [79]. However, they can perhaps not be expected to participate unless they are specifically targeted with up-front financial support to overcome their risk aversion. In earlier studies, the main factors limiting uptake of SALM practices within the KACP were found to be lack of labour to implement SALM practices, knowledge on how to implement them and land availability [41,44]. In addition, women had problems finding the time to attend training [43,44].

#### *4.5. KACP Farms Had Higher Savings and Food Su*ffi*ciency*

Kisumu farmers were able to save on average more than Bungoma farmers. This was most likely partly due to more off-farm income opportunities in Kisumu and partly because the yield was lower in Kisumu, so farmers needed cash savings to buy food. On average, project farmers saved to a larger extent, more often and in larger amounts than control farmers, probably because the VSLA concept in the KACP provided opportunities to save (Figure 7a,b). The VSLA methodology has earlier shown positive results for several household indicators, such as the development of income-generating enterprises and access to education and food [80]. Preliminary data from a study in 2016 also indicated higher income from tree products, better fuelwood access and higher milk yield among the KACP farmers compared with neighbouring control areas [42]. All of these can increase the scope of cash savings by households.

Project farmers at both sites had significantly higher food sufficiency than control farmers. This can be directly related to differences in maize yield, but maize is just one of many food crops. The improved food sufficiency may also be attributable to other farm commodities such as sorghum, beans and cassava, yields of which probably also benefit from terracing and other SALM practices. Improved yields and better food sufficiency among the KACP farmers have been reported in an earlier study of women and men farmers [44]. The higher level of food sufficiency in Bungoma most likely reflects the greater farm size and higher average yields in this area than in Kisumu. Agroforestry practices could have added to food sufficiency through, for example, fruits. Earlier studies report maintained yields in combination with such additional benefits from agroforestry [81].

#### *4.6. Limitations of the Study*

It is a strength that control farms were included in the study, rather than just monitoring changes over time from the baseline on the project farms. However, the study has some limitations partly related to the control farms. A possibility of farmers participating in the KACP being different, perhaps better-off, than those who did not participate emerged early in the project, so an attempt was made to avoid potential self-selection bias by adding a control group of farmers in 2012. However, this meant that no control farms were monitored from the beginning of the project and there is uncertainty in the data on maize yield based on control farmer recall for the first three years (2009–2011). Moreover, uptake of SALM practices on control farms was only assessed in 2012 and thus data were lacking for 2009–2011. The identified control farms were also perhaps too close to project farms, which may have given a non-representative control group. Another limitation of this study was the "weak definitions" of uptake of practices. The uptake data were binary (practiced or not practiced) and the quantities of mulch, manure, etc. applied were not recorded, which created large variation in the effects of farmers' uptake of practices.

#### *4.7. Implications and Recommendations*

Overall, the KACP had a tendency for positive effects regarding uptake and effect of certain SALM practices, development of maize yield, food sufficiency and savings. However, to obtain stronger evidence that the results are due to the intervention, large-scale projects like this would need to include control farms in the design from the beginning, together with clearly defined quantitative indicators of uptake of practices. This could be done jointly by development agencies and research institutions, in order to achieve an optimal design and reduce the costs of monitoring and evaluation. Some of the practices promoted in the KACP were also questionable (e.g., no tillage and careful use of inorganic fertiliser), especially considering the relatively poor target group. A primary aim of the advisory services in such projects should be yield and productivity increases and building resilience, while climate change mitigation objectives should be viewed as a secondary goal [41]. The target group should not be expected to take any risks. Of the SALM practices promoted, only terraces and trees showed positive (but relatively small) effects on yield. Thus, other parts of the advisory services, for example, micro-credit access [82], might have played a greater role in the increased maize yield and savings and need to be explored in future research.

#### **5. Conclusions**

This is the first research study on the yield effects of promoted practices in an agricultural soil carbon project for smallholders in sub-Saharan Africa. Advisory services provided to farmers participating in the KACP appeared to increase the uptake of some of the soil management and tree planting (especially fodder trees) practices and to lower the use of non-sustainable practices. Indications of some uptake through farmer-to-farmer horizontal learning were observed for control farmers.

Higher inclusion of trees (agroforestry) and the use of terraces were the only practices that correlated positively with higher maize yield. The lack of effect of practices was likely due to vague definitions of the practices within the project. Maize yield increased from the start of the KACP and continued over the four years studied and was higher among project farmers than control farmers during all years. The yield increase was similar for project and control farmers but the timing differed, since project farms achieved their main increase during the first years, while control farms obtained their main increase during later years.

Participants in the KACP had higher food self-sufficiency and the results indicated greater ability to save money, both more frequently and in greater amounts. Thus, apart from carbon revenues, the KACP farmers seemed to use more sustainable management practices, had higher maize yield, had better food self-sufficiency and tended to save more money than control farmers. This improved the preparedness of the smallholder farmers participating in the KACP and increased their resilience for future challenges. However, it was not possible to determine whether those farmers were already on this path to improvement before joining the KACP.

**Author Contributions:** Conceptualization, Y.N. and I.Ö.; methodology, I.Ö., Y.N., C.M. and E.W.; software, Y.N., C.M. and E.W.; validation, Y.N., C.M., E.W. and I.Ö.; formal analysis, Y.N.; investigation, C.M. and E.W.; resources, I.Ö.; data curation, Y.N.; writing—original draft preparation, Y.N.; writing—review and editing, Y.N., C.M., E.W., J.W., M.J. and I.Ö.; visualization, Y.N., E.W.; supervision, J.W., M.J. and I.Ö.; project administration, Y.N.; funding acquisition, I.Ö. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Swedish Ministry for Foreign Affairs, as part of its special allocation on global food security; the Formas-Sida programme "Sustainable development in developing countries" (220-2009-2073); and the Swedish University of Agricultural Sciences (SLU).

**Acknowledgments:** Farmers and advisors around Kisumu and Bungoma are gratefully acknowledged for their comprehensive efforts. Special thanks to Bo Lager, Fred Marani, Wangu Mutua, Amos Wekesa, Peter Wachira and the rest of the Vi Agroforestry team. Thanks also to Johannes Forkman, who assisted in statistical analyses.

**Conflicts of Interest:** The authors declare no conflict of interest. All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments, or comparable ethical standards. Informed consent was obtained from all individual participants in the study. The funders had no role in the

design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

### **Appendix A**

**Table A1.** Sustainable Agricultural Land Management (SALM) practices promoted and implemented in the Kenya Agricultural Carbon Project (KACP) (after [31]).

**Table A2.** Characteristics of the Kenya Agricultural Carbon Project (KACP) sites at Kisumu and Bungoma.

1 LM1 = Lower Midland 1; LM2 = Lower Midland 2; LM3 = Lower Midland 3; LM4 = Lower Midland 4; UM1 = Upper midland 1; UM2 = Upper midland 2.

*Land* **2020**, *9*, x FOR PEER REVIEW 18 of 23

**Figure A1***.* Monthly rainfall data in the study areas, 2009-2012; (**a**) Bungoma site (Kakamega meteorological station; total annual rainfall 2009-2012 = 1789, 2113, 1876 and 2287 mm, respectively). (**b**) Kisumu site (Kisumu meteorological station; total annual rainfall 2009-2012 = 1426, 1477, 1563, and 2036 mm, respectively). **Figure A1.** Monthly rainfall data in the study areas, 2009–2012; (**a**) Bungoma site (Kakamega meteorological station; total annual rainfall 2009–2012 = 1789, 2113, 1876 and 2287 mm, respectively). (**b**) Kisumu site (Kisumu meteorological station; total annual rainfall 2009–2012 = 1426, 1477, 1563, and 2036 mm, respectively).

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