**5. Potential of reducing carbon and water footprints for sustainable maize production**

#### **5.1 Maize production and carbon footprint**

Maize cultivation has traditionally relied on conventional tillage methods involving plowing. However, due to factors such as cost, natural conditions, and environmental concerns, there is an increasing adoption of noninversion systems in modern maize production. These noninversion systems typically involve reduced tillage, where no-till seeders are used for substituting plowing and contributing to sequester more soil carbon. By promoting adoption of the no-tillage system, the seeders put seeds directly into uncultivated soil for low disturbance and high reduction of GHG [57].

In order to protect the soil and the environment, the use of noninversion tillage techniques and the retention of a minimum of 30% of plant residues on the field, known as conservation tillage, are of particular importance. These practices help preserve soil structure and reduce erosion while promoting the conservation of organic matter.

Enhancing the management of soil cultivation practices to promote the sequestration of organic carbon in the soil is crucial for mitigating greenhouse gas (GHG) emissions in agriculture. Recent research conducted by Holka et Bienkowski [57] in Wielkopolska in Poland, has highlighted that the adoption of no-tillage methods combined with substantial crop residue retention can effectively reduce GHG emissions in maize production. Irrespective of the specific tillage system utilized, the process of mineral fertilization emerged as the key contributor to GHG emissions. Developing

*Sustainable Maize Production and Carbon Footprint in Arid Land Context: Challenges… DOI: http://dx.doi.org/10.5772/intechopen.112965*

low-emission technologies necessitates careful consideration of the associated risks, particularly related to nitrogen fertilizer usage. To minimize emissions from agricultural fields and simultaneously reduce raw material consumption in fertilizer production, optimizing fertilization practices becomes essential, taking into account natural constraints and soil conditions, as well as the desired crop productivity levels.

By considering the sequestration of organic carbon (C) in the calculation of greenhouse gas (GHG) emissions, the net carbon footprint (CF net) of grain maize production was found to be significantly reduced. Compared to the baseline CF value, the CF net values were lowered by 42.9% in the conventional tillage (CT) system, 72.1% in the reduced tillage (RT) system, and 78.3% in the no-tillage (NT) system. When GHG emissions were analyzed per ton of maize produced, the inclusion of C sequestration showed the most substantial impact in reducing total GHG emissions in the NT and RT systems, with reductions of 78.3% and 72.1%, respectively. Effective management of maize crop residues, such as leaving larger amounts of residues in the field, played a significant role in preventing C losses promoting its sequestration, and reducing the carbon footprint in maize production.

#### **5.2 Maize production and water footprint**

Water footprint (WF) is an indicator that plays a vital role in promoting sustainable maize production by addressing both water consumption and pollution. Maize is a major global crop, and understanding its water footprint is crucial for ensuring responsible water management practices. It provides a new approach for assessing water resource utilization in agriculture.

The WF of crop production serves as a comprehensive indicator that encompasses the various types of water consumption, quantities utilized, and environmental impacts throughout the entire crop growth period [58]. It provides a holistic understanding of water consumption and its associated implications during the process of crop cultivation. The WF takes into account not only the direct water usage by the crops but also the indirect water footprint related to the production and use of inputs such as fertilizers and pesticides.

The WF considers both the blue water footprint (water from surface or groundwater sources) and the green water footprint (rainwater stored in the soil). Additionally, it accounts for the gray water footprint, which refers to the volume of water that is required to assimilate polluted water [59]. In a study conducted by Sun et al. [58] in Beijing, they found that WF had decreasing trends because of the reduced green WF due to the change in climate and the rising temperature and water scarcity, while the gray WF increased because of chemical fertilizers and pesticides. They concluded that the gray WF should be controlled to achieve a sustainable maize production. In another study conducted in Italy by Borsato et al. [60], they affirm that soil conservation tillage systems can reduce the gray WF by 10%. The study focuses on soil tillage systems and variable rate application as means to reduce the gray WF. It emphasizes that the interaction between soil tillage systems and soil management plays a significant role in reducing the gray WF. They found that minimum Tillage with Precision Farming shows lower gray WF values, both in terms of water usage per ton and per hectare. Soil tillage systems combined with variable rate application exhibit a higher reduction in gray WF. To reduce water pollution, prioritizing the reduction of insecticides and herbicides, using chemicals with a lower gray WF, and implementing sustainable soil management practices are recommended.


**BIG CATS**

Edited by **Avadh B. Shrivastav** and **Keshav P. Singh**

#### **Big Cats**

http://dx.doi.org/10.5772/66582 Edited by Avadh B. Shrivastav and Keshav P. Singh

#### **Contributors**

Hussein Abdelhay Essayed Kaoud, Lucas Gonçalves, Edward J Narayan, Nagarajan Baskaran, Janice Vaz, Juan Jose Alava, Miguel Saavedra Mendoza, Paul Cun, Eric Horstman, Sonia Carabajo, Santiago Jimenez Gonzalez, Claudia Jimenez Escobar, Manuel Ruiz-Garcia, Jesus Maldonado, Haydy Monsalve, Ligia M. Jimenez Robayo, Susan L Castro, Jimmy J. Vargas, Sonia R. Quintanilla, Alberto Sanchez, Daniel Veraguas, Diana Echeverry, Fidel Ovidio Castro, Lleretny Rodriguez-Alvarez, A. B. Shrivastav, K.P. Singh

#### **© The Editor(s) and the Author(s) 2017**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

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#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2017 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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Big Cats Edited by Avadh B. Shrivastav and Keshav P. Singh p. cm. Print ISBN 978-953-51-3683-5 Online ISBN 978-953-51-3684-2 eBook (PDF) ISBN 978-953-51-4015-3


**5. Experimental results**

#### **5.1 OpenCL performance on GPU compared the CPU one**

OpenCL offers a convenient way to construct heterogeneous computing systems and opportunities to improve parallel application performance. As first step, the OpenCL SAD kernel was implemented in two platforms: CPU with 4 cores at frequency 2.5 GHz and an NVDIA GPU 920 m of 954 MHz as frequency. The SAD block dimensions are from 4 � 8 to 64 � 64 pixels. A comparative analysis was made on the same video between the CPU and GPU is seen in **Figure 6**. It is clear from the next figure that the GPU execution time is greater than CPU execution (**Figure 7**) [16].

When using the Eq. (2), **Figure 6** indicates the speed up [17] of the both implementations.

$$\text{Speed} - up = \text{CPU execution time} / \text{GPU execution time} \tag{2}$$

The speed up shows that the GPU platform is more efficient than the CPU platform, and this is due to the efficient parallel architecture of GPU compared to CPU. To validate the OpenCL code compared to the CUDA code the next study is proposed.

#### **5.2 Execution performance OpenCL GPU compared to CUDA GPU**

Running the application through GPU requires these steps as it is shown in **Figure 8**. For OpenCL, approach contains GPU detection and kernel compilation.

#### **Figure 6.**

*Performance OpenCL comparison with GPU and CPU platforms.*

**Figure 7.** *Speed-up using OpenCL language.*

The CPU input is read from the host to the device by all frameworks; the kernel is executed on the GPU; the device is returned to the host by copy data. Finally, the results are displayed on CPU.

**Table 2** reports the kernel running time for different size of Prediction Unit (PU) (designed the block size used). In order to get repeated average times, we fixed each problem 10 times for both CUDA and OpenCL.

We use a normalized performance metric, called Performance (PR), to compare the performance of CUDA and OpenCL (**Figure 9**).

$$PR = UDA \text{ execution time} / OpenCL \text{ execution time} \tag{3}$$

If performance ratio is greater than 1, OpenCL will give a better results compared to CUDA language. As shown in **Figure 8**, the performance ratio indicates that the OpenCL kernel running time is better than CUDA kernel running for each *Performance Analysis of OpenCL and CUDA Programming Models for the High Efficiency Video… DOI: http://dx.doi.org/10.5772/intechopen.99823*

#### **Figure 8.**

*Algorithm flow.*

#### **Table 2.**

*GPU and CPU application running times in seconds.*

size block. Similar results are obtained by Frang et al. [18] and Exterman [19], respectively.

#### **5.3 Comparative study**

In this section, we compared the time performance of our proposed implementation to State-of-the-Art process [20, 21].

In the work presented by Xiao et al. [20], when comparing the result of the proposed with the HEVC reference software, experimental results show that the proposed GPU implementation achieves 34.4% encoding time reduction on average while the BD-rate increase is only about 2% for a typical low delay setting. Another interesting work is proposed by Karimi et al. [21] used a specific real-world application to compare the performance of CUDA with NVIDIA's implementation of

#### **Figure 9.** *Performance ratio.*

OpenCL. Contrary to our results, CUDA's kernel execution was here consistently faster than OpenCl's, despite the two implementations running nearly identical code. CUDA seems to be a better choice for applications where achieving as high a performance as possible is important. Otherwise the choice between OpenCL and CUDA can be made by considering factors such as prior familiarity with either system, or available development tools for the target GPU hardware. The performance will be dependent on some variables, including code quality, algorithm type and hardware type.


**A.2.1 Direct measurement**

The theory developed in previous paragraph is a theory of Raman amplification. This means that to measure Raman gain, we should perform experiments on Raman amplifiers [47, 48]. Several materials, such as silicon, allow a direct measurement of SRS [49, 50]. In this case the Raman gain can be evaluated by measuring the Stokes amplification in a Raman amplifier having as active medium the material under test [2].

In the steady-state (no pump depletion) regime of SRS, assuming no losses at the Stokes frequency, the value of the gain coefficient *g* can be obtained by fitting Eq. (8), which is readily transformed into:

$$\text{SRS Gain} = \mathbf{10} \ast \log\_{10} \left( \frac{I\_{\rm S}(L)}{I\_{\rm S}(0)} \right) \\ = \mathbf{4.34} \ast \mathbf{g} \ast L \ast I\_{\rm P}(\mathbf{0}) \tag{8}$$

where *IS* (0) is the intensity of the input Stokes radiation (Stokes seed), *IS*(*L*) is the intensity of the output Stokes radiation, *IP*(0) = \_\_*P A* is the intensity of the input pump radiation, *P* is the power incident onto the sample, *A* as the effective area of pump beam and *L* is the effective length. Since the sample is transparent to the incident light, *L* is taken to be equal to the thickness of the sample along the path of the incident light.

As an example, in **Figure 5** a typical trend of the maxima of the signal power plotted as a function of the effective pump power at the exit of Raman amplifier is reported. As the laser power increases, the SRS gain is first constant and then grows approximately linear when the power is greater than the threshold value and so stimulated scattering begins to prevail. The threshold is usually defined as the power at which the linear behavior starts, while the slope of the line is proportional to the Raman gain coefficient *g*. The estimation of the Raman gain coefficient *g* is not straightforward due to the uncertainty in the effective focal volume inside the sample [36, 39, 42].

**139**

**Author details**

where ( ∂2 \_\_\_\_\_\_ σ

**Conflict of interest**

∂*Ω*∂ω*S*)0

(IMM), Napoli, Italy

provided the original work is properly cited.

Maria Antonietta Ferrara\* and Luigi Sirleto

*Stimulated Raman Scattering in Micro- and Nanophotonics*

*R*(ω*S*) = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ ω*S* [*N*(ω*S*,*T*) + 1] (ω*P* − ω)

where *ωS* is the Stokes Raman shift (in cm−1

cross section is expressed by the following equation:

*g*(ω*S*) = λ*S*

If Raman gain is weak and the length of sample is small, Raman amplification is difficult to measure and an indirect measurement should be implemented. Frequently, this happens for glasses, for which the spontaneous Raman spectra is firstly measured by a standard Raman set up, then the Raman gain is estimated by a

In order to eliminate in the measured Stokes Raman intensity *I*(*ω*) its dependence on both the temperature and the frequency of the vibrational modes [51, 52],

frequency, *N*(*ωS*,*T*) is the Bose-Einstein mean occupation number and *T* is the temperature [31]. Afterwards, with the aim to properly relate the Raman spectra of investigated glasses with the standard silica glass, the measured Raman spectra can be adjusted also for the differences in reflection and angle of collection [26, 27, 31]. The relation between Raman gain spectrum and spectral and differential Raman

> 3 \_\_\_\_\_ *c*2*hn*2 ( ∂2 \_\_\_\_\_\_ σ

the thermal population factor), *λS* is the Stokes wavelength (in m), *c* is the velocity of light in vacuum and *n* is the refraction index at the excitation wavelength [24–27, 31].

The authors declare that they have no competing financial interests.

∂*Ω*∂ω*S*)0

is the Raman cross section at *T* = 0 K (i.e., corrected considering

4 *I*(ω*S*) (9)

(10)

units), *ωP* is the laser excitation

*DOI: http://dx.doi.org/10.5772/intechopen.80814*

**A.2.2 Indirect measurement**

numerical procedure [2].

the following relation can be used:

© 2018 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,

National Research Council (CNR), Institute for Microelectronics and Microsystems

\*Address all correspondence to<EMAIL_ADDRESS>
**Figure 5.** *A typical trend of SRS signal plotted as a function of the effective pump power.*


**Abstract**

This chapter is intended to present to readers a general scope of the technical, theoretical, and numerical applications of computational fluid dynamics using the finite volume method, restricted to incompressible turbulent flows (Ma < 0.3). The main objective of this chapter was to provide readers of a starting point to select an adequate numerical model for the flow regime of interest. Such knowledge could be a key at the moment of extending the analysis to more complex problems, for example, the ones found in heat transfer and fluid flows, multiphase flows, and compressible flows.

**Keywords:** CFD, finite volume technique, circular cylinder, large eddy simulation, conservation equations