**Applications of Ionizing Radiation in Mutation Breeding**

Özge Çelik and Çimen Atak

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66925

#### **Abstract**

As a predicted result of increasing population worldwide, improvements in the breeding strategies in agriculture are valued as mandatory. The natural resources are limited, and due to the natural disasters like sudden and severe abiotic stress factors, excessive floods, etc., the production capacities are changed per year. In contrast, the yield potential should be significantly increased to cope with this problem. Despite rich genetic diversity, manipulation of the cultivars through alternative techniques such as mutation breeding becomes important. Radiation is proven as an effective method as a unique method to increase the genetic variability of the species. Gamma radiation is the most preferred physical mutagen by plant breeders. Several mutant varieties have been successfully introduced into commercial production by this method. Combinational use of *in vitro* tissue culture and mutation breeding methods makes a significant contribution to improve new crops. Large populations and the target mutations can be easily screened and identified by new methods. Marker assisted selection and advanced techniques such as microarray, next generation sequencing methods to detect a specific mutant in a large population will help to the plant breeders to use ionizing radiation efficiently in breeding programs.

**Keywords:** mutation breeding, in vitro mutagenesis, gamma rays, molecular markers, high-throughput technologies

#### **1. Introduction**

The worldwide population is expected to be nine billion at 2050. Conventional agricultural crops are inadequate to meet the current need to provide sustainable yield production. Therefore, crop improvement is getting an important need when we are not able to meet the demands of growing world population. For this reason, humans have begun to develop new

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plant varieties for cultivation, and it is called as plant breeding. Numerous food, feed, and ornamental and industrial crops were improved via hybridization methods to meet the needs of human beings since many years. Over the last 15 years, development of new techniques became useful in breeding strategies to facilitate the improvement of new crop varieties.

Plant breeding methods and recent progress in biotechnology contribute greatly to friendly agriculture. The main point is to establish productive breeding strategies to improve crops.

Variation is the main point of the breeding that the plant breeders are focused. Genetic variation is a natural phenomenon. This variation is a natural result of genotypes, which have interactions with the environmental facts, get together. The recombination and independent assortment of the alleles are responsible to obtain new individuals from the population. Domestication of the crops is affected by several conditions such as ecological and agricultural. Selection of the adaptive genotypes is getting important in breeding of the cultivars. The main point is to achieve the production of higher-yielding crops [1], useful traits such as size of the fruits, and quality of the crops. The aim of the breeding is to combine various features of many plants in one plant. This method is general for breeding of the plants via sexual reproduction. During recombination of the alleles, offsprings carrying selectable variations for the several traits exist. Recombination is not responsible to produce new traits itself. Although genetic changes have provided the natural variation for species evolution, changes in species have not only been important for adaptation to natural environment. Mutations are the main reasons of genetic variabilities and cause new species eventually. Therefore, they have also been exploited by man in the agricultural processes of species domestication and crop improvement. As a new approach, manipulation of the cultivars through alternative techniques such as mutation breeding and Biotechnology are useful for especially some fully sterile plants [2].

Mutations have been shown as a way of procreating variations in a variety. They spontaneously occur in nature. Several mistakes can cause mutations during replication process. On the other hand, radiation is an efficient mutagen that the plants are exposed. The important point is the origin of the mutated cell. Somatic cell mutations are not easily traceable and cannot pass to the future generations; otherwise, embryonic cell mutations directly pass to the next springs. Spontaneous mutations occur without any human intervention and happen randomly with a low frequency. However, some mutagenic agents are known to induce mutations as an alternative to low incidences of spontaneous mutations to increase genetic variability by increasing the frequency of mutations. Using of mutagens propose the possibility of inducing desired characters that cannot be found in nature, in a variety or lost during the evolution [2].

A mutation is defined as any change within the genome of an organism, and it is not brought on by normal recombination and segregation [3]. The direct use of mutation is a very valuable supplementary approach to plant breeding. The main advantage of this technique is the shorter time required to breed a crop with improved character(s) than the hybridization process to obtain the same results.

Induced mutations consequently have a high potential for bringing about further genetic improvements. Induced mutations have played a significant role in meeting challenges related to world food and nutritional security by way of mutant germplasm enhancement and their utilization for the development of new mutant varieties. A wide range of genetic variability has been induced by mutagenic treatments for use in plant breeding and crop improvement programs [1]. Physical mutagens are generally preferred by reason of being convenient, easily reproducibility, and user-/environment-friendly method. Ionizing radiation is used as a physical mutagen in breeding applications.

#### **2. Types of ionizing radiation**

plant varieties for cultivation, and it is called as plant breeding. Numerous food, feed, and ornamental and industrial crops were improved via hybridization methods to meet the needs of human beings since many years. Over the last 15 years, development of new techniques became useful in breeding strategies to facilitate the improvement of new crop varieties.

Plant breeding methods and recent progress in biotechnology contribute greatly to friendly agriculture. The main point is to establish productive breeding strategies to improve crops.

Variation is the main point of the breeding that the plant breeders are focused. Genetic variation is a natural phenomenon. This variation is a natural result of genotypes, which have interactions with the environmental facts, get together. The recombination and independent assortment of the alleles are responsible to obtain new individuals from the population. Domestication of the crops is affected by several conditions such as ecological and agricultural. Selection of the adaptive genotypes is getting important in breeding of the cultivars. The main point is to achieve the production of higher-yielding crops [1], useful traits such as size of the fruits, and quality of the crops. The aim of the breeding is to combine various features of many plants in one plant. This method is general for breeding of the plants via sexual reproduction. During recombination of the alleles, offsprings carrying selectable variations for the several traits exist. Recombination is not responsible to produce new traits itself. Although genetic changes have provided the natural variation for species evolution, changes in species have not only been important for adaptation to natural environment. Mutations are the main reasons of genetic variabilities and cause new species eventually. Therefore, they have also been exploited by man in the agricultural processes of species domestication and crop improvement. As a new approach, manipulation of the cultivars through alternative techniques such as mutation breeding and Biotechnology are useful for especially some fully

Mutations have been shown as a way of procreating variations in a variety. They spontaneously occur in nature. Several mistakes can cause mutations during replication process. On the other hand, radiation is an efficient mutagen that the plants are exposed. The important point is the origin of the mutated cell. Somatic cell mutations are not easily traceable and cannot pass to the future generations; otherwise, embryonic cell mutations directly pass to the next springs. Spontaneous mutations occur without any human intervention and happen randomly with a low frequency. However, some mutagenic agents are known to induce mutations as an alternative to low incidences of spontaneous mutations to increase genetic variability by increasing the frequency of mutations. Using of mutagens propose the possibility of inducing desired

characters that cannot be found in nature, in a variety or lost during the evolution [2].

A mutation is defined as any change within the genome of an organism, and it is not brought on by normal recombination and segregation [3]. The direct use of mutation is a very valuable supplementary approach to plant breeding. The main advantage of this technique is the shorter time required to breed a crop with improved character(s) than the hybridization

Induced mutations consequently have a high potential for bringing about further genetic improvements. Induced mutations have played a significant role in meeting challenges related

sterile plants [2].

112 New Insights on Gamma Rays

process to obtain the same results.

Ionizing radiation (IR) is categorized by the nature of the particles or electromagnetic waves that create the ionizing effect. These have different ionization mechanisms and may be grouped as directly or indirectly ionizing. The physical properties of ionizing radiation types, namely gamma rays X-rays, UV light, alpha-particles, beta-particles, and neutrons, are different; therefore, their potential usage and bioapplicability to the breeding programs are different.

In the beginning of the twentieth century, ionizing radiation has been begun to induce the mutations. They can be particulate or electromagnetic (EM). Their specific feature is the localized release of large amounts of energy. These have different ionization mechanisms, and they can group as directly or indirectly ionizing. The physical and chemical reactions initiate the biological effects of ionizing radiations [4].

Mostly, X-rays had been used, and later gamma rays and neutrons have been preferred. Two forms of electromagnetic radiation, X-rays or gamma (γ) rays, are widely used in biological systems and most clinical applications. Cobalt-60 and cesium-137 (Cs-137) are the main sources of gamma rays used in biological studies. Cesium-137 is more preferred since its half-life is much longer than cobalt-60. Gamma rays are produced spontaneously, whereas X-rays are produced in an X-ray tube (accelerated electrons hit a tungsten target, and then they are decelerated. The Bremsstrahlung radiation is part of the kinetic energy, belongs to the electrons, and is converted to X-rays). Energy transfer is caused by the interaction, it cannot completely displace an electron, and it produces an excited molecule/atom; whenever the energy of a particle or photon exceeds the ionization grade of a molecule, ionization occurs. Ten electronvolt binding energy for the electrons is determined for biological materials, and higher energetic photons are considered as ionizing radiation, whereas the energies between 2 and 10 eV, which cause excitation, are called as nonionizing. Electrons, protons, α-particles, neutrons, and heavy charged ions are clinically used natural radiation types [4–6].

#### **2.1. Effects of ionizing radiations**

Ionizing radiation (IR) is known to effect on plants. Their effects are classified as direct and indirect. Stimulatory, intermediate, and detrimental effects on plant growth and development are based on dose of ionizing radiation applied to the plant tissues. The main point is to evaluate the impacts of ionizing radiation at genetic level. The severity of the impacts of radiation is in relation with the species, cultivars, plant age, physiology, and morphology of the plants besides their genetic organizations.

Ionizing radiation causes structural and functional changes in DNA molecule, which have roles in cellular and systemic levels. The nature of DNA modifications includes base alterations, base substitutions, base deletions, and chromosomal aberrations. These modifications are the reasons of macroscopic phenotyping variations [5, 6].

Interaction between atoms or molecules and ionizing radiations causes free radical production that damages the cells. Free radical is defined as an atom or group of atoms including an unpaired electron. Water in the cell accumulates energy initially and facilitates the production of reactive radicals, which oxidize and reduce. They have a role in direct and indirect actions of ionizing radiations. In direct action, a secondary electron reacts directly with the target to produce an effect, while in indirect action, free radicals produced via radiolysis of water interact with the target to comprise target radicals [6, 7].

There are substantial data indicating that the lethal effects of radioactive compounds accumulate in nucleus rather than other parts. Therefore, DNA is the main target as a result of ionizing radiation, and it targets DNA directly or indirectly and leads various alterations. Direct ionization of DNA, reactions with electrons or solvated electrons, reactions with OH or H<sup>2</sup> O+ , and reactions with other radicals can damage cellular DNA. There are some possibilities of DNA damages caused by ionizing radiation. IR and secondarily produced reactive oxygen species can cause changes in deoxyribose ring and structures of bases, DNA-DNA cross-links, and DNA-protein cross-links. Hydroxyl radicals react with bases. The reactive intermediates are produced as a result of this interaction [7, 8].

Hydroxyl radicals separate hydrogen atoms from the sugar-phosphate backbone of DNA to form 2-deoxyribose radical, which cause strong damages via attacking to oxygen or thiol groups [8]. Researchers have shown that purine and pyrimidine rings, single-strand breaks (SSBs), and base loss regions are damaged by DNA radiolysis products induced by free radicals. The amount of the yield of the individual products is important and reported to be different than produced during oxidative metabolism. Although free radicals attack on DNA and cause several DNA damages, they have not been thought to lead lethal and mutagenic results. Ionizing radiation-induced base damages are widely studied by in vitro studies. It is also reported by several studies that direct and indirect radiation effects may produce identical reactive intermediates. Oxygen is another key molecule that determines the biological effectiveness of the ionizing radiation. Oxygen can easily react with many free radicals. The amount of the radicals presents in deoxyribose or bases; harmful DNA damages occur [9–11].

If the damage site is deoxyribose, a strand brake directly forms. DNA base damages like ring saturation destabilize the N-glycosidic bonds, and abasic deoxyribose residues form. These regions can be converted into strand breaks. Double-strand breaks (DSBs) happen as a result of a localized attack by two or more OH radicals on DNA. Another potential reason can be defined as a hybrid attack that OH damages one of the strands, whereas the other strand exposes to a direct damage within 10 base pairs of the hydroxyl radical [12]. IR leads chromosomal aberrations during cell division. Chromosome malsegregation and defects in chromatid separation, bridge formation, chromosome exchange, chromosome breakage, and loss of chromosome fragments can be observed after IR treatment [13].

We can classify IR as an abiotic stress factor; therefore, the plants represent different levels of adaptive responses. DNA repair mechanisms and adaptive responses against radiation could protect the plant genome from excessive modifications. Natural ionizing radiation is supposed to play a significant role in the evolution of the plants. Homolog recombinations between the chromosomes would result in formation of new altered generations that show specific adaptive capabilities [13].

#### **3. Mutation breeding**

Ionizing radiation causes structural and functional changes in DNA molecule, which have roles in cellular and systemic levels. The nature of DNA modifications includes base alterations, base substitutions, base deletions, and chromosomal aberrations. These modifications

Interaction between atoms or molecules and ionizing radiations causes free radical production that damages the cells. Free radical is defined as an atom or group of atoms including an unpaired electron. Water in the cell accumulates energy initially and facilitates the production of reactive radicals, which oxidize and reduce. They have a role in direct and indirect actions of ionizing radiations. In direct action, a secondary electron reacts directly with the target to produce an effect, while in indirect action, free radicals produced via radiolysis of water

There are substantial data indicating that the lethal effects of radioactive compounds accumulate in nucleus rather than other parts. Therefore, DNA is the main target as a result of ionizing radiation, and it targets DNA directly or indirectly and leads various alterations. Direct ionization of DNA, reactions with electrons or solvated electrons, reactions with OH or H<sup>2</sup>

and reactions with other radicals can damage cellular DNA. There are some possibilities of DNA damages caused by ionizing radiation. IR and secondarily produced reactive oxygen species can cause changes in deoxyribose ring and structures of bases, DNA-DNA cross-links, and DNA-protein cross-links. Hydroxyl radicals react with bases. The reactive intermediates

Hydroxyl radicals separate hydrogen atoms from the sugar-phosphate backbone of DNA to form 2-deoxyribose radical, which cause strong damages via attacking to oxygen or thiol groups [8]. Researchers have shown that purine and pyrimidine rings, single-strand breaks (SSBs), and base loss regions are damaged by DNA radiolysis products induced by free radicals. The amount of the yield of the individual products is important and reported to be different than produced during oxidative metabolism. Although free radicals attack on DNA and cause several DNA damages, they have not been thought to lead lethal and mutagenic results. Ionizing radiation-induced base damages are widely studied by in vitro studies. It is also reported by several studies that direct and indirect radiation effects may produce identical reactive intermediates. Oxygen is another key molecule that determines the biological effectiveness of the ionizing radiation. Oxygen can easily react with many free radicals. The amount of the radicals presents in deoxyribose or bases; harmful DNA damages occur [9–11].

If the damage site is deoxyribose, a strand brake directly forms. DNA base damages like ring saturation destabilize the N-glycosidic bonds, and abasic deoxyribose residues form. These regions can be converted into strand breaks. Double-strand breaks (DSBs) happen as a result of a localized attack by two or more OH radicals on DNA. Another potential reason can be defined as a hybrid attack that OH damages one of the strands, whereas the other strand exposes to a direct damage within 10 base pairs of the hydroxyl radical [12]. IR leads chromosomal aberrations during cell division. Chromosome malsegregation and defects in chromatid separation, bridge formation, chromosome exchange, chromosome breakage, and

loss of chromosome fragments can be observed after IR treatment [13].

O+ ,

are the reasons of macroscopic phenotyping variations [5, 6].

114 New Insights on Gamma Rays

interact with the target to comprise target radicals [6, 7].

are produced as a result of this interaction [7, 8].

In nature, mutations acquired new survival traits to the crops against environmental stresses both biotic and abiotic. Many of these survival traits could be weakened or totally lost in time. Mutations are sudden changes at the genotype level and cause small and exquisite changes in phenotype, which cannot be detected by advanced molecular techniques. Identification of naturally mutated gene is inconvenient. When the breeders pinpoint the mutated gene, wild-type features have to be reestablished. This task is becoming increasingly infeasible due to long time, more human source, and increase in cost. That's why new breeding strategies were needed to be improved to fortify the crops. To achieve this mission, plant breeders should rebuild in crop plants several specific traits, which have role in survival of the plants under extreme conditions providing the other crop-specific traits such as quality, yield, etc. Phenotyping-based processes of conventional breeding strategies should have moved from base to a high level of genotype-based breeding methods [1, 14]. New technologies should be legal, economic, and ethical for the breeders and the consumers.

Under such circumstances, inducing mutations are potential applications to produce crops with desired traits and easily selected from the germplasm pool. As described above, radiation can cause several effects on genetic material due to the exposure dose. These effects can be classified in both positive and negative approaches. Beside the detrimental effects of radiation, plant breeders are focused on the effective usage of gamma radiation in breeding programs. Changes in agronomic characters can be transmitted to the next generations. Nuclear techniques are begun to be used in plant breeding mostly for inducing mutations. During the past 60 years, we observed a significant increase in the major crops. Ionizing radiations such as X-rays and gamma rays have been used for improvement of several crops such as wheat, rice, barley, cotton, tobacco, beans, etc. [15]. Plant breeders are also combined with this resource with different techniques to increase the efficiency and shorten the time. Induced mutagenesis and combined breeding strategies are effective to improve quantitative and qualitative traits in crops in a much shorter time than the conventional breeding procedure [6].

Gamma radiation is widely used to induce mutations in breeding studies than chemical mutagens. Ionizing radiation could cause several DNA damages randomly; therefore, several mutations (from point mutation to chromosome aberrations) could be induced. Over 3000 mutant varieties of major crops have been reported to be developed by ionizing radiation [2, 16].

Mutation rate/mutation frequency is defined as the ratio of mutation per locus and also termed as the number of mutant plant per M<sup>2</sup> generation [16]. It changes due to per dose and mutagen. The main point is to determine the best dose for inducing mutants rather than its type. From past to present, it is concluded that the doses between LD50 and LD30 (doses lead to 50% and 30% lethality) are generally useful in mutation breeding programs. The importance of convenient dose that depends on the radiation intensity and exposure time is gestured by the researchers [6].

The final target is to select the desired mutants in the second and third generations (M<sup>2</sup> and M3 ). It is effective to select the mutants treated by the mutagens with a high mutation frequency from the M<sup>1</sup> population. M<sup>1</sup> population consists of heterozygous plants. That means during the treatment one allele is affected by the mutation, and it is impossible to discriminate the recessive mutation in this generation. Therefore, the breeders should sift out the next generations to identify the homozygotes for both dominant and recessive alleles [6]. M<sup>2</sup> population is the first generation that the selection begins. Physical, mechanical, phenotypic, and other methods are used for the selection of the mutants. When the plant breeder finds a mutant line, the next step is the multiplication of the seeds for further field and other studies. The main theme is to develop a mutant, which has a potential to be commercial variety surpassing the mother cultivar or a new genetic stock having improved properties.

According the 2015 data of Food and Agriculture Organization/International Atomic Energy Agency (FAO/IAEA), over 232 different crops including wheat, rice, sunflower, soybean, tomato, and tobacco were subjected to mutation breeding programs and over 3000 mutant varieties with improved properties in over 70 countries [6]. The mutant plant production distribution worldwide is given in **Figure 1**.

Sixty-one percent of these varieties was improved by using gamma radiation. **Figure 2** represents the maximum plant species improved via mutation breeding.

 **Figure 1.** The number and the rate of the mutant cultivar production rate worldwide [17].

 **Figure 2.** The maximum plant species improved via mutation breeding [17].

gen. The main point is to determine the best dose for inducing mutants rather than its type. From past to present, it is concluded that the doses between LD50 and LD30 (doses lead to 50% and 30% lethality) are generally useful in mutation breeding programs. The importance of convenient dose that depends on the radiation intensity and exposure time is gestured by

The final target is to select the desired mutants in the second and third generations (M<sup>2</sup>

passing the mother cultivar or a new genetic stock having improved properties.

sents the maximum plant species improved via mutation breeding.

 **Figure 1.** The number and the rate of the mutant cultivar production rate worldwide [17].

population. M<sup>1</sup>

tribution worldwide is given in **Figure 1**.

). It is effective to select the mutants treated by the mutagens with a high mutation fre-

during the treatment one allele is affected by the mutation, and it is impossible to discriminate the recessive mutation in this generation. Therefore, the breeders should sift out the next generations to identify the homozygotes for both dominant and recessive alleles [6]. M<sup>2</sup> population is the first generation that the selection begins. Physical, mechanical, phenotypic, and other methods are used for the selection of the mutants. When the plant breeder finds a mutant line, the next step is the multiplication of the seeds for further field and other studies. The main theme is to develop a mutant, which has a potential to be commercial variety sur-

According the 2015 data of Food and Agriculture Organization/International Atomic Energy Agency (FAO/IAEA), over 232 different crops including wheat, rice, sunflower, soybean, tomato, and tobacco were subjected to mutation breeding programs and over 3000 mutant varieties with improved properties in over 70 countries [6]. The mutant plant production dis-

Sixty-one percent of these varieties was improved by using gamma radiation. **Figure 2** repre-

population consists of heterozygous plants. That means

and

the researchers [6].

116 New Insights on Gamma Rays

quency from the M<sup>1</sup>

M3

Mutation breeding studies are widely preferred to improve cultivars tolerant or resistant to various abiotic stresses and biotic stresses such as bacteria, viruses, and pathogens and to improve the quality and the agricultural traits of the crops such as oil, protein, and yield [6]. The most improved features in some plant species by gamma radiation were given in **Table 1**.

Instead of waiting for natural mutations to generate a desired trait, creating a mutation with different tools may promote to the breeding studies. The simplicity and low cost of mutation treatments and gamma radiation became an effective tool to improve new agronomic traits in various crops. It may be evaluated as an alternative to genetically modified plants. The released mutation breeding-derived varieties showed the potential usage of mutation breeding as a flexible and available accession to any crop supplied for desired purposes, and discriminating techniques are successfully combined.

As mentioned above, mutation breeding studies are provided to numerous researches in terms of developing applications for plant biotechnology, plant tissue culture, and mutation treatments to improve new cultivars. Therefore, research and developmental studies are widely associated to combined techniques including in vitro culture and molecular techniques through mutation breeding. In vitro mutagenesis applications are becoming important at this point.

#### **3.1. In vitro mutagenesis applications**

Induced mutagenesis is a widely used method to identify and isolate the plant genes in combination with molecular accessions. These kinds of studies supply a clear prehension into the relation of genes and functions of the genes that have role in growth and development under several conditions [12].


**Table 1.** Applications of induced mutagenesis for improved features in plant breeding [6, 17].

In vitro culture methods appear to have opportunities to display the useful variants. The recent improvements on in vitro technology acquired an importance to enlarge the aim of mutation breeding applications. The use in conjunction of in vitro tissue culture and mutation breeding methods makes a significant contribution to improve new crops and new varieties. Jain [18] reported the importance of this technique for ornamental plants beside the crops.

It is known that genetic variabilities may occur during in vitro culture conditions without any application of mutagens, spontaneously. The frequency of the variants still indeterminable, and there are many parameters to depend on. Application of the mutagens can increase the rate of genetic variability via inducing the frequency of the mutations.

The progress in recombinant DNA technologies and genes can be easily cloned from a genome into a genome of an organism. Genes can be purified in vitro in small amounts, and therefore the potential of inducing mutations has significantly broadened. In a controlled experimental environment, it is available to change the sequence of the nucleotides of DNA. In vitro mutagenesis studies systemically and efficiently focus on the potential ways of inducing mutagenesis. Some applications of mutagenesis depend on using isolated DNA molecule. In contrast to conventional mutagenesis, in vitro mutation breeding can be thought as a practicable and achievable technique to improve new genetic variabilities. Only few traits can be modified, and the remaining is not altered by the treatment.

In vitro mutagenesis have some properties such as increased mutation rate, uniform mutagen treatment, needs of less space and time for large populations, and opportunity to keep the plant material disease-free. On the other hand, one of the main restrictions of mutation breeding application is the formation of chimeras as a result of the treatment. At this point, mutant selection process is becoming important.

In vitro culture methods are more useful in mutation studies. Totipotency is a natural feature of the plant cells. By using one plant cell, it is possible to produce a whole plant, induce regeneration of the tissues and micropropagation of the plants in large volumes, and give opportunity to use different parts of the plants (stem, leaves, cuttings, apical and axillary buds, and tubers) to induce mutagenesis easily. Another advantage of in vitro culture is to screen the populations after mutagenesis to select the variant/mutants before giving a whole plant. Different plant tissues can be propagated to produce different tissues by using several combinations of plant growth regulators. Callus is an important cell organization. Cell suspension culture technique is started by using callus tissue to separate the single totipotent cells. Every plant cell can be differentiated into somatic embryos which is a useful tool for mutagenesis [4, 9].

The target of the studies is to isolate the non-chimeric mutants from the irradiated explants to obtain desired mutants via repetitive selection processes. Meanwhile, duration of the culture and the selective traits that mutated are the main factors effect these processes [4]. M<sup>2</sup> generation of the culture is the earliest step that the predominantly recessive mutants could be determined. Mutagen treatment can be applied at different stages of the cultures.

#### *3.1.1. Selection of species and explant types for in vitro culture*

In vitro culture methods appear to have opportunities to display the useful variants. The recent improvements on in vitro technology acquired an importance to enlarge the aim of mutation breeding applications. The use in conjunction of in vitro tissue culture and mutation breeding methods makes a significant contribution to improve new crops and new varieties. Jain [18] reported the importance of this technique for ornamental plants beside

Apple Early maturing, red fruit skin color, variegated leaf, dwarf, compact tree, resistance to powdery

Banana Earliness, bunch size, reduced height, tolerance to *Fusarium oxysporum* f. sp. cubense, large fruit size,

It is known that genetic variabilities may occur during in vitro culture conditions without any application of mutagens, spontaneously. The frequency of the variants still indeterminable,

the crops.

**Crops Improved traits**

Barley Phytate (antinutrient) Canola Oil quality improvement

Indian jujube Fruit morphology, earliness

Mung bean Resistant to yellow mosaic virus Pineapple Spineless, drought tolerance

Rapeseed Resistant to powdery mild

Salinity tolerance

Wheat Resistant to stripe rust

Apricot Earliness

118 New Insights on Gamma Rays

Loquat Fruit size

mildew and apple scab

Chickpea Resistant to Ascochyta blight and Fusarium wilt

Tomato Resistant to bacterial wilt (*Ralstonia solanacearum*)

more plant oil, low amylose, low protein)

Sunflower Oil quality improvement, semidwarf/dwarf cultivars

Cotton Resistant to bacterial blight, cotton leaf curl virus, high fiber

putative mutant resistant to black sigatoka disease

Citrus Seedless, red color fruit, *Xanthomonas citri* disease resistant, resistant to tristeza virus

Maize Resistant to pathogen *Striga*, acidity, and drought tolerance; improvement of protein quality

Rice Resistant to blast, yellow mottle virus, bacterial leaf blight and bacterial leaf stripe, semidwarf/dwarf

Soybean Resistant to Myrothecium leaf spot and yellow mosaic virus, oil quality improvement, oilseed meals

Strawberry Thick and small leaf, light leaf color, white flesh and long fruit, *Phytophthora cactorum* resistance

that are low in phytic acid desirability, poultry and swine feed

**Table 1.** Applications of induced mutagenesis for improved features in plant breeding [6, 17].

cultivar, lodging resistance, acid sulfate soil tolerance; tolerant to cold and high altitudes, salinity tolerance, early maturity, high-resistant starch in rice for diabetes patients, giant embryos of eight

> Correct choice of the plant species due to economic, commercial production capacity and agricultural importance is the first step of an in vitro mutagenesis study. The selection of the plant material is related to the success of the in vitro culture. Seed, callus, node, shoot, and root tip cultures are the most commonly preferred plant material for in vitro mutagenesis applications. The genotype of a plant has a role in in vitro culture studies. The studies showed that different explants of same plant had different responses to the same radiation dose [19, 20]. Therefore, it is necessary to design an in vitro mutagenesis experiment in a proper combination of dose and explant type.

#### *3.1.2. Determination of proper gamma radiation dose*

The most important subject of in vitro mutagenesis is to select the suitable radiation dose to obtain the maximum viability. In the beginning, assessment of the LD50 value is needed to optimize the exact mutation dose. The sensitivity of the plants changes due to the species, cultivars, and current physiological environment. A preliminary dose experiment should be performed to define the appropriate dose. Reduced growth and seedling damages may be seen as traces of the genetically damaged plants after irradiation [21]. IAEA/FAO reported the average doses for crop species and summarized in **Table 2**.

According to the findings of the preliminary studies done with gamma radiation, it has been reported that there is no linearity between the radiation dose and the variance. The experimental gamma radiation treatments were summarized in **Table 3**.

Seed, callus, shoot tips, node cultures, and bulblets were frequently used for irradiation of different species. 137Cs and 60Co gamma sources were used to induce mutagenesis at different doses depending on the radiosensitivity of the explants. Atak et al. [24] used 100–500 Gy radiation doses produced by 137Cs gamma source for soybean seeds, while Singh and Datta [29] used 60Co gamma source at different doses ranging between 10 and 100 Gy for *Triticum aestivum* seeds. Ulukapı et al. [21] also used 60Co gamma source at 80–240 Gy radiation doses to induce genetic variability for *Solanum melongena* L. Çelik and Atak [23] used 100, 200, 300, and 400 Gy gamma rays by 137Cs to determine the effective radiation dose for breeding studies of two Turkish tobacco varieties. They irradiated the tobacco seeds and selected the salt-tolerant mutants in M<sup>3</sup> progeny.

Seetohul et al. [35] used 0–60 Gy gamma doses of 60Co gamma source to induce mutations for shoot tip explants of Taro plant. Jain [26] irradiated shoot tip explants of *Musa* spp. by Cesium-137 at 10–50 Gy doses, while Baraka and El-Sammak [33] used 0.25–1 Gy for *Gypsophila paniculata* L. shoot tip explants by 60Co gamma source. Atak et al. [25] used shoot tip explants of *Rhododendron* varieties to induce mutants at 5–50 Gy of gamma rays of 137Cs source.

In tissue culture treatments, different synthetic chemicals show similar effects as plant growth regulators which have abilities to induce growth of the tissues as desired. In mutation-based


**Table 2.** Gamma radiation radiosensitivity of some crop species [22].


**Table 3.** In vitro mutagenesis protocols for some crop species.

selection of the plants with desired characters using in vitro cultivation methods for vegetative plants, clonally reproduction of the plant parts is needed in order to detect the mutant generations via using easy stability tests [27]. The schematic diagram representing the usage of gamma radiation for in vitro mutagenesis applications is given in **Figure 3**.

#### *3.1.3. In vitro selection of the mutants*

*3.1.2. Determination of proper gamma radiation dose*

120 New Insights on Gamma Rays

average doses for crop species and summarized in **Table 2**.

mental gamma radiation treatments were summarized in **Table 3**.

The most important subject of in vitro mutagenesis is to select the suitable radiation dose to obtain the maximum viability. In the beginning, assessment of the LD50 value is needed to optimize the exact mutation dose. The sensitivity of the plants changes due to the species, cultivars, and current physiological environment. A preliminary dose experiment should be performed to define the appropriate dose. Reduced growth and seedling damages may be seen as traces of the genetically damaged plants after irradiation [21]. IAEA/FAO reported the

According to the findings of the preliminary studies done with gamma radiation, it has been reported that there is no linearity between the radiation dose and the variance. The experi-

Seed, callus, shoot tips, node cultures, and bulblets were frequently used for irradiation of different species. 137Cs and 60Co gamma sources were used to induce mutagenesis at different doses depending on the radiosensitivity of the explants. Atak et al. [24] used 100–500 Gy radiation doses produced by 137Cs gamma source for soybean seeds, while Singh and Datta [29] used 60Co gamma source at different doses ranging between 10 and 100 Gy for *Triticum aestivum* seeds. Ulukapı et al. [21] also used 60Co gamma source at 80–240 Gy radiation doses to induce genetic variability for *Solanum melongena* L. Çelik and Atak [23] used 100, 200, 300, and 400 Gy gamma rays by 137Cs to determine the effective radiation dose for breeding studies of two Turkish tobacco

varieties. They irradiated the tobacco seeds and selected the salt-tolerant mutants in M<sup>3</sup>

of *Rhododendron* varieties to induce mutants at 5–50 Gy of gamma rays of 137Cs source.

**Species Useful mutation breeding dose (gray)**

*Oryza sativa* japonica 120–250 *Oryza sativa* indica 150–250 *Triticum aestivum* 40–70 *Hordeum vulgare* 30–60 *Glycine max* 100–200 *Phaseolus vulgaris* 80–150 *Nicotiana tabacum* 200–350 *Medicago sativa* 400–600

**Table 2.** Gamma radiation radiosensitivity of some crop species [22].

Seetohul et al. [35] used 0–60 Gy gamma doses of 60Co gamma source to induce mutations for shoot tip explants of Taro plant. Jain [26] irradiated shoot tip explants of *Musa* spp. by Cesium-137 at 10–50 Gy doses, while Baraka and El-Sammak [33] used 0.25–1 Gy for *Gypsophila paniculata* L. shoot tip explants by 60Co gamma source. Atak et al. [25] used shoot tip explants

In tissue culture treatments, different synthetic chemicals show similar effects as plant growth regulators which have abilities to induce growth of the tissues as desired. In mutation-based

progeny.

The selection of the desired mutants is an essential and important part in a mutation breeding program. In vitro mutagenesis applications give opportunity to the breeders to select the mutants in a controlled environment. The plant breeders can work with a large population of plant material. Different culture techniques such as suspension cultures and protoplast cultures can be widely preferred to have a genetic uniformity in the selection studies.

 **Figure 3.** The representative schematic diagram of an in vitro mutagenesis application.

In vitro selection studies have some advantages. These can be classified as follows:


In vitro selection studies can be performed in two types: single step and multistep [4]. In single-step selection procedure, the inhibitor agent is added to the culture environment and the subcultures used for the selection studies. In multistep method, the dose of the selective agent below lethal dose is added to the culture, and the concentration of the inhibitor is gradually increased in subcultures. The selected mutant by this method has been defined as more stable than selected via other methods [4].

Food and ornamental plants are widely assessed for nutritional quality, early ripening, better flower, and biotic/abiotic stress tolerance capacities [4]. For abiotic stress treatments, it is more convenient to control the culture conditions than in the field environment [20, 40]. Salt, drought, cold, and heavy metal tolerance have been successfully performed in many plants. Callus, suspension cultures, or protoplast cultures were used for in vitro selection analysis by adding the selective agents reducing the growth such as mannitol and polyethylene glycol for drought tolerance; NaCl for salt tolerance; boron, aluminum, and nickel for metal tolerance; or changing the temperature of the cultures to select cold/high-temperature-tolerant plants [4, 41]. Both selection strategies, single step and multistep, can be used. The main point is to inhibit the false-positive selection responses due to epigenetic alterations in long-term culture conditions. When the plants are subject to long-term stress treatments with gradual increase of the selective agent, non-tolerant cells can experience stable epigenetic alterations, which can be inherited by mitosis. In order to avoid this period, preference of single-step selection procedure is suggested to be efficient during mutation breeding programs [41].

In selection studies, the main criterion is to define the exact selective agent. This means that the molecular mechanism of the desired trait should be clearly understood by the plant breeders. Morphological and physiological changes should be used in combination to discriminate the mutants. All the parameters such as leaf injury, slower growth, average number of shoots per explant, survival percentage of the plants, fresh weight of the explants, leaf photosynthetic capacity, antioxidant defense system, and accumulation of osmolytes should be investigated in detail especially for stress tolerance studies [40, 41].

#### **3.2. Mutational genomic analysis**

In vitro selection studies have some advantages. These can be classified as follows:

In vitro selection studies can be performed in two types: single step and multistep [4]. In single-step selection procedure, the inhibitor agent is added to the culture environment and the subcultures used for the selection studies. In multistep method, the dose of the selective agent below lethal dose is added to the culture, and the concentration of the inhibitor is gradually increased in subcultures. The selected mutant by this method has been defined as more stable

Food and ornamental plants are widely assessed for nutritional quality, early ripening, better flower, and biotic/abiotic stress tolerance capacities [4]. For abiotic stress treatments, it is more convenient to control the culture conditions than in the field environment [20, 40]. Salt, drought, cold, and heavy metal tolerance have been successfully performed in many plants. Callus, suspension cultures, or protoplast cultures were used for in vitro selection analysis by adding the selective agents reducing the growth such as mannitol and polyethylene glycol for drought tolerance; NaCl for salt tolerance; boron, aluminum, and nickel for metal tolerance; or changing the temperature of the cultures to select cold/high-temperature-tolerant plants [4, 41]. Both selection strategies, single step and multistep, can be used. The main point is to

**3.** Availability to use some selective agents in culture conditions

 **Figure 3.** The representative schematic diagram of an in vitro mutagenesis application.

**1.** Easiness of the application

122 New Insights on Gamma Rays

**2.** Reduced time of the selection

than selected via other methods [4].

Mutational genomics is becoming a valuable tool to differentiate the mutants improved via mutation breeding programs. It is also an important tool to understand the molecular basis of the plant stress response based on the data gathered from mutants of model plants and an easy way to determine the genetic similarities and characterize the variations between the mutants at the DNA level.

The mutants were identified based on morphological characters, traditionally. The new developments in DNA technologies give opportunity to the plant breeders to make it quick and definite.

Molecular markers are widely used to differentiate the genetic differences between the mutant and the mother plants through characterizing the variations at DNA level. High-throughput genomic platforms such as random amplified DNA polymorphism (RAPD), cDNA-amplified fragment length polymorphism (AFLP), single-strand conformational polymorphism (SSCP), microarray, differential display, targeting induced local lesions in genome (TILLING) and high-resolution melt (HRM) analysis allow rapid and in-depth global analysis of mutational variations [4].

Among these methods RAPD, inter simple sequence repeat (ISSR), and AFLP have been frequently used in genomic classification of the mutants [15, 42]. RAPD is an inexpressive and a rapid method to use in many fields of biotechnology. There is no need for genome information. It has been widely used to determine the genetic diversity in mutation breeding programs of many plants. RAPD is an efficient method to detect DNA alteration via using random primers. It has been started to use in earlier studies of genetic variabilities obtained by radiation treatments in *Chrysanthemum* [36, 37], soybean [24], sugarcane, sunflower, groundnut [43], tobacco [23], potato [27], *Rhododendron* [25]. ISSR method is another molecular marker method widely used in plant biotechnology applications. It is also easy to apply more informative than RAPD, reliable, and inexpensive [44, 45]. ISSR primers are designed by using microsatellite sequences to amplify the genomic regions flanked by microsatellite repeats. By using one primer, it is possible to amplify multiple fragments as a result of ISSR analysis [46, 47]. The information obtained from ISSR analysis is more reliable than RAPD to provide supplementary data of the genetic variations of the mutants from the nonoverlapping genome regions [48].

Xi et al. [38] reported an in vitro mutagenesis protocol for *Lilium longiflorum* Thunb. cv. White fox. They used 0, 0.5, 1.0, 1.5, 2.0, and 2.5 Gy gamma rays to observe the effects of radiation on adventitious bud formation from bulblet-scale thin cell layers. 1.0 Gy was determined as the most effective dose due to survival rate of the bulblet-scale thin layers. They also evaluated the morphological mutants using ISSR DNA fingerprinting method.

Sianipar et al. [49] used RAPD method to detect the genetic variability between the mutant plantlets improved from gamma-irradiated rodent tuber calli. They obtained 69 fragments from 11 mutant plantlets by using 10 RAPD primers.

Barakat and El-Sammak [33] irradiated shoot tips and lateral buds of *G. paniculata* with four different gamma radiation doses between 0.25 and 1 Gy. They detected the genetic polymorphisms among the mutants by RAPD analysis. They obtained 105 different amplification products from 10 random primers. RAPD is evaluated as an efficient molecular marker technique to detect the variations. Atak et al. [25] used RAPD method to show the genetic similarities of the *Rhododendron* mutants improved via gamma irradiation. They used 0–50 Gy gamma radiation doses to improve the shoot and root regeneration rates of *Rhododendron* plants. RAPD detected higher genetic variability among the *Rhododendron* mutants. Yaycılı and Alikamanoğlu [27] observed 89.66% polymorphism rate with six primers among the mutant potato plants, which were improved as salt tolerant via gamma radiation treatment. Kaul et al. [36] used in vitro mutagenesis in *Chrysanthemum* cv. Snow Ball by irradiation of the in vitro shoots, and genetic polymorphisms among the mutants and the control plants were assessed by RAPD. They reported that 10 Gy gamma irradiation was found as the most effective dose to induce genetic variation in morphological traits, and they observed 100% polymorphism among the mutants. Gamma radiation-induced salt-tolerant oriental tobacco mutants were improved by Çelik and Atak [23]. Salt tolerance of the mutants was controlled by the callus induction in the presence of high salt concentration. The genetic similarities of the mutants were determined by RAPD analysis. The relationships between the salt-tolerant mutants and controlled tobacco varieties were shown in Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram. Some representative RAPD profiles of the mutants developed by in vitro mutagenesis were given in **Figure 4**.

Sen and Alikamanoğlu [44] used ISSR method to differentiate the drought-tolerant sugar beet mutant improved via irradiation of shoot tip explants by gamma radiation. They obtained 91 polymorphic bands of 106 PCR fragments with 19 inter simple sequence repeat (ISSR) primers.

Perera et al. [40] applied in vitro mutagenesis treatment to an important energy crop giant miscanthus (*Mischanthus* × *giganteus*) to induce variation in cultivar Freedom. ISSR markers were used to determine the variations in the mutant plants. The putative mutants were selected due to the results of molecular marker analysis to use for further bioenergy researches. Wu et al. [50] used ISSR analysis to show the genetic similarities between the mutants. For this reason, they used 60 ISSR primers, and 60 polymorphic bands of 392 were evaluated to have information on the molecular level of mutation breeding. Atak et al. (unpublished data) [51] used ISSR marker method (with 61 ISSR primers) to define the genetic variation among the 8 salt-tolerant mutant soybeans obtained from in vitro mutagenesis treatment by using 137Cs gamma source. The representative results of ISSR amplification of 8 salt-tolerant mutant soybeans were given in **Figure 5**.

Xi et al. [38] reported an in vitro mutagenesis protocol for *Lilium longiflorum* Thunb. cv. White fox. They used 0, 0.5, 1.0, 1.5, 2.0, and 2.5 Gy gamma rays to observe the effects of radiation on adventitious bud formation from bulblet-scale thin cell layers. 1.0 Gy was determined as the most effective dose due to survival rate of the bulblet-scale thin layers. They also evaluated

Sianipar et al. [49] used RAPD method to detect the genetic variability between the mutant plantlets improved from gamma-irradiated rodent tuber calli. They obtained 69 fragments

Barakat and El-Sammak [33] irradiated shoot tips and lateral buds of *G. paniculata* with four different gamma radiation doses between 0.25 and 1 Gy. They detected the genetic polymorphisms among the mutants by RAPD analysis. They obtained 105 different amplification products from 10 random primers. RAPD is evaluated as an efficient molecular marker technique to detect the variations. Atak et al. [25] used RAPD method to show the genetic similarities of the *Rhododendron* mutants improved via gamma irradiation. They used 0–50 Gy gamma radiation doses to improve the shoot and root regeneration rates of *Rhododendron* plants. RAPD detected higher genetic variability among the *Rhododendron* mutants. Yaycılı and Alikamanoğlu [27] observed 89.66% polymorphism rate with six primers among the mutant potato plants, which were improved as salt tolerant via gamma radiation treatment. Kaul et al. [36] used in vitro mutagenesis in *Chrysanthemum* cv. Snow Ball by irradiation of the in vitro shoots, and genetic polymorphisms among the mutants and the control plants were assessed by RAPD. They reported that 10 Gy gamma irradiation was found as the most effective dose to induce genetic variation in morphological traits, and they observed 100% polymorphism among the mutants. Gamma radiation-induced salt-tolerant oriental tobacco mutants were improved by Çelik and Atak [23]. Salt tolerance of the mutants was controlled by the callus induction in the presence of high salt concentration. The genetic similarities of the mutants were determined by RAPD analysis. The relationships between the salt-tolerant mutants and controlled tobacco varieties were shown in Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram. Some representative RAPD profiles of the

Sen and Alikamanoğlu [44] used ISSR method to differentiate the drought-tolerant sugar beet mutant improved via irradiation of shoot tip explants by gamma radiation. They obtained 91 polymorphic bands of 106 PCR fragments with 19 inter simple sequence repeat (ISSR) primers. Perera et al. [40] applied in vitro mutagenesis treatment to an important energy crop giant miscanthus (*Mischanthus* × *giganteus*) to induce variation in cultivar Freedom. ISSR markers were used to determine the variations in the mutant plants. The putative mutants were selected due to the results of molecular marker analysis to use for further bioenergy researches. Wu et al. [50] used ISSR analysis to show the genetic similarities between the mutants. For this reason, they used 60 ISSR primers, and 60 polymorphic bands of 392 were evaluated to have information on the molecular level of mutation breeding. Atak et al. (unpublished data) [51] used ISSR marker method (with 61 ISSR primers) to define the genetic variation among the 8 salt-tolerant mutant soybeans obtained from in vitro mutagenesis treatment by using 137Cs gamma source. The representative results of ISSR amplification of 8 salt-tolerant mutant soy-

the morphological mutants using ISSR DNA fingerprinting method.

mutants developed by in vitro mutagenesis were given in **Figure 4**.

beans were given in **Figure 5**.

from 11 mutant plantlets by using 10 RAPD primers.

124 New Insights on Gamma Rays

 **Figure 4.** Evaluation of salt-tolerant tobacco mutants improved via in vitro mutagenesis application. A and B represent the RAPD profiles of the mutants. C shows the callus growth of control plants under in vitro salt stress. D shows the callus growth profiles of the mutants under salt stress [23].

**Figure 5.** The representative results of evaluation of 8 salt-tolerant mutant soybean plants improved by in vitro mutagenesis treatment. A.The callus gowth of Ataem-7 and S04-05 soybean cultivars. B. The callus gowth of Ataem-7 and S04-05 soybean cultivars under 90 mM NaCl. C. Callus growth of M5 under 90 mM NaCl D. The whole plant M5.

Single-strand conformational polymorphism (SSCP) is another strength method to identify the variations between the mutant and mother plants in amplified DNA samples. It is widely used to determine the genetic mutations in several organisms. It is also an effective method to find a potential genetic marker which is in relation with a desired trait to use in selection studies of agricultural populations [52]. Irradiation of the plant tissues can cause mutation between the allelic gene copies [single-nucleotide polymorphism (SNP)]. SSCP is an efficient method to detect these polymorphisms. It is possible to detect relations between SSCP polymorphisms and quantitative traits [53].

These methods can only be able to detect the genetic variations of the mutants in accordance with the mother plants. There are a number of methods to screen the causal mutation at a desirable phenotype. Molecular markers that are in relation with the mutation are known to be able to segregate in the next progenies. The main point is to make the functional analysis of the mutant genes that have role in acquiring the new desired characters. To identify a mutant, the number of the genes controlling that specific phenotypic character is deterministic [54].

In a mutation breeding program, identification of differentially expressed genes, the biological processes they have role in, or the metabolic pathways of interest should be carried out through modern genomics and system biology. To achieve this, there are specific tools to discriminate with the use of next-generation molecular techniques. In microarray systems, it is available to detect the gene expressional differences between the mutants and control plants. Thousands of spots on a microarray chip containing a few million copies of identical DNA molecules buried on each spot are related to each gene of a plant genome. If it is a targeted mutation, it is possible to show the expressional differences between them by microarray technique. In general, spontaneous mutations cannot be detected at microarray systems. Sequencing methods are more efficient in the meanwhile. Mutant plants can now easily sequence by next-generation sequencing (NGS) techniques to define the mutations [55]. To apply these methods, there is no need for a reference genome. These analyses can be classified as forward genetic screening methods that give opportunity to improve the knowledge about the genes that control specific biological roles in mutant plants. In contrast to forward genetic, reverse genetic is more popular to detect the function of a gene. In mutation breeding programs, the plant breeders are focused to identify the individuals from a population that have an allelic variation of a gene. As mentioned previously, these individuals are improved by mutagenic treatments. TILLING method is available to determine the mutants with specific phenotypes. In tomato, approximately 3000 mutant lines that were improved by chemical mutagens on fruit ripening trait were identified by this method. This method is used for barley to screen the homeodomain-leucine zipper protein mutants. Recent progresses in NGS technologies and TILLING which is in relation with these technologies make it possible to screen the potential genes [54, 56].

#### **4. Discussion and the conclusion**

The increasing importance of plant breeding studies in correlation with biotechnology and molecular genetics is attempted to meet the requirements of increasing population for food and crop plants. Therefore, mutation breeding treatments have become more frequent and alternative to classical breeding and genetically modified plants. The main aim is to combine several features of many plants in one super plant. In vitro mutagenesis has become an efficient tool for this purpose. Plant breeders are focused to crop improvement techniques to improve genetic variations of useful traits by using next-generation molecular methods.

Using these advanced genomic techniques, new molecular mechanisms and new genes can be potentially identified by the plant breeders as a result of in vitro mutagenesis treatments. To gain more data, additional needs of various comparative and descriptive experiments can be upgraded to acquire more specific points to build the relations between the regulatory mechanisms. Therefore, the recent progress in mutation breeding studies in relation with new technologies is quite important to contribute new advancement to plant breeding programs.

#### **Author details**

used to determine the genetic mutations in several organisms. It is also an effective method to find a potential genetic marker which is in relation with a desired trait to use in selection studies of agricultural populations [52]. Irradiation of the plant tissues can cause mutation between the allelic gene copies [single-nucleotide polymorphism (SNP)]. SSCP is an efficient method to detect these polymorphisms. It is possible to detect relations between SSCP poly-

These methods can only be able to detect the genetic variations of the mutants in accordance with the mother plants. There are a number of methods to screen the causal mutation at a desirable phenotype. Molecular markers that are in relation with the mutation are known to be able to segregate in the next progenies. The main point is to make the functional analysis of the mutant genes that have role in acquiring the new desired characters. To identify a mutant, the number of the genes controlling that specific phenotypic character is deterministic [54]. In a mutation breeding program, identification of differentially expressed genes, the biological processes they have role in, or the metabolic pathways of interest should be carried out through modern genomics and system biology. To achieve this, there are specific tools to discriminate with the use of next-generation molecular techniques. In microarray systems, it is available to detect the gene expressional differences between the mutants and control plants. Thousands of spots on a microarray chip containing a few million copies of identical DNA molecules buried on each spot are related to each gene of a plant genome. If it is a targeted mutation, it is possible to show the expressional differences between them by microarray technique. In general, spontaneous mutations cannot be detected at microarray systems. Sequencing methods are more efficient in the meanwhile. Mutant plants can now easily sequence by next-generation sequencing (NGS) techniques to define the mutations [55]. To apply these methods, there is no need for a reference genome. These analyses can be classified as forward genetic screening methods that give opportunity to improve the knowledge about the genes that control specific biological roles in mutant plants. In contrast to forward genetic, reverse genetic is more popular to detect the function of a gene. In mutation breeding programs, the plant breeders are focused to identify the individuals from a population that have an allelic variation of a gene. As mentioned previously, these individuals are improved by mutagenic treatments. TILLING method is available to determine the mutants with specific phenotypes. In tomato, approximately 3000 mutant lines that were improved by chemical mutagens on fruit ripening trait were identified by this method. This method is used for barley to screen the homeodomain-leucine zipper protein mutants. Recent progresses in NGS technologies and TILLING which is in relation with these technologies make it possible to

The increasing importance of plant breeding studies in correlation with biotechnology and molecular genetics is attempted to meet the requirements of increasing population for food and crop plants. Therefore, mutation breeding treatments have become more frequent and alternative to classical breeding and genetically modified plants. The main aim is to com-

morphisms and quantitative traits [53].

126 New Insights on Gamma Rays

screen the potential genes [54, 56].

**4. Discussion and the conclusion**

Özge Çelik\* and Çimen Atak

\*Address all correspondence to: ocelik@iku.edu.tr

Department of Molecular Biology and Genetics, Faculty of Science and Letters, TC İstanbul Kultur University, Istanbul, Turkey

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Provisional chapter

### **Neutron-Stimulated Gamma Ray Analysis of Soil** Neutron-Stimulated Gamma Ray Analysis of Soil

DOI: 10.5772/68014

Aleksandr Kavetskiy, Galina Yakubova, Stephen A. Prior and Henry Allen Torbert Aleksandr Kavetskiy, Galina Yakubova,

Stephen A. Prior and Henry Allen Torbert

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/68014

#### Abstract

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10.1093/mp/ssu136

132 New Insights on Gamma Rays

This chapter describes technical aspects of neutron-stimulated gamma ray analysis of soil carbon. The introduction covers general principles, different modifications of neutron-gamma analysis, measurement system configuration, and advantages of this method for soil carbon analysis. Problems with neutron-gamma technology in soil carbon analysis and methods of investigations including Monte-Carlo simulation of neutron interaction with soil elements are discussed further. Based on the investigation results, a method of extracting the "soil carbon net peak" from the raw acquired data was developed. The direct proportional dependency between the carbon net peak area and average carbon weight percent in the upper 10 cm soil layer for any carbon depth profile was shown. Calibration of the measurement system using sand-carbon pits and field measurements of soil carbon are described. Measurement results compared to chemical analysis (dry combustion) data demonstrated good agreement between the two methods. Thus, neutron-stimulated gamma ray analysis can be used for in situ determination of near-surface soil carbon content and is applicable for precision geospatial mapping of soil carbon.

Keywords: soil carbon analysis, neutron-stimulated gamma ray analysis, Monte-Carlo simulation, Geant4, soil carbon mapping

#### 1. Introduction

#### 1.1. System evolution and application

Neutron-gamma analysis is based on detection of gamma lines that appear due to neutronnuclei interactions. Many nuclei can be detected and quantified by the presence of these characteristic gamma lines. State-of-the-art nuclear physics methodologies and instrumentation, combined with commercial availability of portable pulse neutron generators, high-efficiency gamma detectors, reliable electronics, and measurement and data processing software, have currently

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

made the application of neutron-gamma analysis possible for routine measurements in various fields of study. For these reasons, material analysis using characteristic gamma rays induced by neutrons is more wide-spread today; e.g., threat material detection (explosives, drugs, and dangerous chemicals [1]), diamond detection [2], planetary science applications for obtaining bulk elemental composition information, soil elemental (isotopic) content and density distribution [3], archaeological site surveying and provenance studies [4, 5], elemental composition of human [6, 7] and animal [8, 9] bodies, real-time elemental analysis of bulk coal on conveyor belts [10, 11], chloride content of reinforced concrete [12, 13], and in oil well logging [14].

In addition to the aforementioned applications, neutron-stimulated gamma ray analysis can be used in soil science for in situ measurements of soil carbon. This method is based on detecting 4.44- MeV gamma rays issued from carbon nuclei excited by fast neutrons promptly after the interaction [15]. Accurate quantification of soil carbon is important since it is an indicator of soil quality [16] that can affect soil carbon sequestration, fertility, erosion, and greenhouse gas fluxes [17–20].

Use of this method for soil elemental analysis has additional advantages over traditional laboratory chemical methods. This is a nondestructive in situ method of analysis that requires no sample preparation and can perform multielemental analyses of large soil volumes that are negligibly impacted by local sharp changes in elemental content. These advantages support the use of the neutron-gamma method in soil science.

#### 1.2. General principles of neutron-gamma analysis

Neutron-gamma analysis is based on nuclei issuing gamma rays upon interaction with neutrons (Figure 1). Gamma rays are issued due to different processes of neutron-nuclei interactions. First of all, there are inelastic neutron scattering (INS) and thermal neutron capture (TNC) where gamma rays are issued promptly after interaction. New radioactive isotopes can appear due to INS and TNC processes, and decay of these isotopes is accompanied by delay activation (DA) gamma rays.

Figure 1. Main processes of neutron interaction with nuclei.

Each kind of nucleus and process produces gamma-rays of particular energy. In some cases, this characteristic gamma line of particular energy can serve as an analytical line for elemental determination. For some elements, the energy of characteristic gamma lines of nuclei and the processes responsible for the appearance of these gamma lines are listed in Table 1. As can be seen, gamma ray energy lies in the 1–11 MeV range. This is the range (greater than 1.022 MeV) where the effect of pair production as gamma rays interact with matter is significant. This is why


Element/nucleus Applied for analysis

made the application of neutron-gamma analysis possible for routine measurements in various fields of study. For these reasons, material analysis using characteristic gamma rays induced by neutrons is more wide-spread today; e.g., threat material detection (explosives, drugs, and dangerous chemicals [1]), diamond detection [2], planetary science applications for obtaining bulk elemental composition information, soil elemental (isotopic) content and density distribution [3], archaeological site surveying and provenance studies [4, 5], elemental composition of human [6, 7] and animal [8, 9] bodies, real-time elemental analysis of bulk coal on conveyor belts

In addition to the aforementioned applications, neutron-stimulated gamma ray analysis can be used in soil science for in situ measurements of soil carbon. This method is based on detecting 4.44- MeV gamma rays issued from carbon nuclei excited by fast neutrons promptly after the interaction [15]. Accurate quantification of soil carbon is important since it is an indicator of soil quality [16] that can affect soil carbon sequestration, fertility, erosion, and greenhouse gas fluxes [17–20].

Use of this method for soil elemental analysis has additional advantages over traditional laboratory chemical methods. This is a nondestructive in situ method of analysis that requires no sample preparation and can perform multielemental analyses of large soil volumes that are negligibly impacted by local sharp changes in elemental content. These advantages support

Neutron-gamma analysis is based on nuclei issuing gamma rays upon interaction with neutrons (Figure 1). Gamma rays are issued due to different processes of neutron-nuclei interactions. First of all, there are inelastic neutron scattering (INS) and thermal neutron capture (TNC) where gamma rays are issued promptly after interaction. New radioactive isotopes can appear due to INS and TNC processes, and decay of these isotopes is accompanied by delay

Each kind of nucleus and process produces gamma-rays of particular energy. In some cases, this characteristic gamma line of particular energy can serve as an analytical line for elemental determination. For some elements, the energy of characteristic gamma lines of nuclei and the processes responsible for the appearance of these gamma lines are listed in Table 1. As can be seen, gamma ray energy lies in the 1–11 MeV range. This is the range (greater than 1.022 MeV) where the effect of pair production as gamma rays interact with matter is significant. This is why

[10, 11], chloride content of reinforced concrete [12, 13], and in oil well logging [14].

the use of the neutron-gamma method in soil science.

1.2. General principles of neutron-gamma analysis

Figure 1. Main processes of neutron interaction with nuclei.

activation (DA) gamma rays.

134 New Insights on Gamma Rays

Table 1. Gamma lines used in neutron-gamma analysis of some elements.

the single escape (SE) and double escape (DE) peaks appear in the gamma spectra near the full energy peak [21]. For example, with carbon registration at full energy peak of 4.44 MeV, peak shifts of 0.511 MeV (SE, 3.93 MeV) and 1.022 MeV (DE, 3.42 MeV) can be observed. The cross sections of INS process for 14-MeV neutron interactions with nuclei are demonstrated in Table 1. For instance, the value of the 12C cross section at neutron energy of 14.1 MeV is ~0.42 barn. The inelastic scattering of fast neutrons on 12C nuclei elevates them to the 4.44-MeV exited energy state [22]. Exited state 12C\* promptly returns to the ground state issuing the 4.44-MeV gamma ray.

$$\text{In} + \, ^{12}\text{C} \to \, ^{12}\text{C}^\* + n' \to \, ^{12}\text{C} + \gamma (4.44 \text{ MeV}) \tag{1}$$

Neutrons lose their energy when propagating through the medium. The interaction cross section depends on energy. The dependence of the INS process cross section with energy for 12C is demonstrated in Figure 2. The intensity or peak area of this gamma line in the spectrum can be associated with the amount of carbon in a given soil volume. Thus, the registration of the gamma spectra from the studied object caused by neutron interaction with its nuclei can be used for elemental analysis of the object.

#### 1.3. Measurement system configuration

The configuration of a measurement system for neutron-gamma analysis should consist of a neutron source, gamma detector, shielding and construction materials, operational electronics, and data acquisition software. Below we briefly consider the main features of these component parts.

#### 1.3.1. Neutron sources

Isotope neutron sources (based on Cf-252, Am-241-Be, Pu-238-Be isotopes) and portable neutron generators can be used in the measurement setup; some commercially available neutron sources are listed in Table 2. Although radioisotope sources are widely used in neutrongamma analysis [23–26], the use of a neutron generator is preferred (from a radiation safety point of view) since no radiation is produced when the generator is turned "off." Furthermore, the availability of pulse neutron generators has significantly expanded the possibilities of this method [1, 2, 11, 27].

Figure 2. Inelastic neutron scattering cross section of 12C nuclei [22].



Table 2. Some neutron sources available for use in neutron-gamma analysis.

#### 1.3.2. Gamma detectors

Type of source Nuclear reaction Time of work = T1/2

Figure 2. Inelastic neutron scattering cross section of 12C nuclei [22].

Be!12C+n

Be!12C+n

Be!12C+n

Be!12C+n

Isotope 241Am/Be 241Am!α+237Np

136 New Insights on Gamma Rays

Neutron Generator α+<sup>9</sup>

239Pu/Be 239Pu!α+235U α+<sup>9</sup>

210Po/Be 210Po!α+206Pb α+<sup>9</sup>

226Ra/Be 226Ra!α+222Rn α+<sup>9</sup>

Genie 16 d+d!n+<sup>3</sup>

Genie 35 d+d!n+<sup>3</sup>

P 211 d+d!n+<sup>3</sup>

P 385 d+d!n+<sup>3</sup>

D 711 d+d!n+<sup>3</sup>

or working mode

252Cf spontaneous fission 252Cf 2.65 yr (alpha decay) 2.3 (6) 4.4e7 [22, 28, 31]

d+t! n+α 14.1 1e10

d+t! n+α 14.1

d+t! n+α 14.1

d+t! n+α 14.1

He On-Off 2.5 2e8 [32]

He On-Off 2.5 1e8 [32]

He On-Off 2.5 1e8 [33]

He On-Off 2.5 5e8 [33]

He On-Off 2.5 2e10 [33]

Neutron energy, MeV, Avg (max)

432.6 yr 4 (11) 4e7 [22, 28, 29]

24100 yr 4.5 (10.7) 4e6 [22, 28, 30]

138 d 4.2 (10.9) 2.5e6 [22, 28]

1600 yr 3.9 (13.1) 1.5e7 [22, 28]

Flux, n/s Reference

Gamma detectors used in neutron-gamma analysis systems should be suitable for operation in mixed radiation fields where neutrons and gamma rays are present. In ideal cases, detectors should have the following properties [1]:


Satisfaction of these requirements can be difficult, especially due to budget constraints. Among the different types of gamma scintillators, inorganic scintillators are more suitable for neutrongamma analysis systems due to higher efficiency of registering gamma rays in the energy range up to 12 MeV. The high sensitivity of inorganic scintillation detectors is assured by high gamma ray energy deposition in the relatively large volume (up to several cubic decimeters) of transparent inorganic gamma scintillator mono-crystals with a high Z and density and by their high light yield values (photons per MeV). Semiconductor detectors have a better resolution compared to scintillation detectors, but lower registration efficiency in the desired energy range (up to 12 MeV), which makes scintillation detectors more preferable for use in neutrongamma analysis systems.

Properties of detectors [based on the sodium iodide NaI(Tl), bismuth germinate BGO, and lanthanum bromide LaBr3(Ce)] commonly used in the neutron-gamma analysis systems are


described in Table 3. Note that other detectors based on inorganic scintillators have worse characteristics and are not usually applied in neutron-gamma analysis.

\* Effective atomic number is calculated by [40].

Table 3. Properties of gamma detectors used in neutron-gamma analysis.

#### 1.3.2.1. NaI(Tl)

As shown in Table 3, all listed detectors have high light yield. These detectors with sizes around dia 15 · 15 cm provide practically 90% adsorption of gamma rays with energy up to 10 MeV as shown by data in Figure 3 for sodium iodide detectors. For other detectors, sizes can be less due to higher density and effective atomic numbers. Sodium iodide detectors under neutron irradiation are activated, showing the delayed beta decay spectral continuum with end point energy of 2 MeV [1]. But this activation by neutron fluxes in neutron-gamma analysis does not significantly impact the neutron-stimulated gamma spectra [41]. There are no significant differences in energy resolution before and after irradiation by 4.7 · 1011 of 14-MeV neutrons for the dia 10 · 10 cm NaI(Tl) detector [42]. The radiation damage of sodium iodide occurs at an adsorption dose of 500–1000 Gy [43], which is not accumulated in real time when conducting neutron-gamma analysis.

Figure 3. The family of curves derived from NBS circular 583 (1956), Table 37, mass attenuation coefficients for NaI(Tl). Each curve represents the percent absorption (I-attenuation) of a parallel beam of gamma rays normally incident on NaI (Tl) crystals of a given thickness [44].

#### 1.3.2.2. BGO

described in Table 3. Note that other detectors based on inorganic scintillators have worse

Resolution, % (at 662 keV)

NaI(Tl) 38000 250 7 3.67 47 [35] Bi4Ge3O12 (BGO) 8200 300 10 7.13 62 [36, 37] LaBr3(Ce) 70000 17 3 5.07 43 [38, 39]

Density, g/cm<sup>3</sup> Effective atomic number\*

Reference

As shown in Table 3, all listed detectors have high light yield. These detectors with sizes around dia 15 · 15 cm provide practically 90% adsorption of gamma rays with energy up to 10 MeV as shown by data in Figure 3 for sodium iodide detectors. For other detectors, sizes can be less due to higher density and effective atomic numbers. Sodium iodide detectors under neutron irradiation are activated, showing the delayed beta decay spectral continuum with end point energy of 2 MeV [1]. But this activation by neutron fluxes in neutron-gamma analysis does not significantly impact the neutron-stimulated gamma spectra [41]. There are no significant differences in energy resolution before and after irradiation by 4.7 · 1011 of 14-MeV neutrons for the dia 10 · 10 cm NaI(Tl) detector [42]. The radiation damage of sodium iodide occurs at an adsorption dose of 500–1000 Gy [43], which is not accumulated in real time when

Figure 3. The family of curves derived from NBS circular 583 (1956), Table 37, mass attenuation coefficients for NaI(Tl). Each curve represents the percent absorption (I-attenuation) of a parallel beam of gamma rays normally incident on NaI

characteristics and are not usually applied in neutron-gamma analysis.

Scintillation decay time, ns

Table 3. Properties of gamma detectors used in neutron-gamma analysis.

1.3.2.1. NaI(Tl)

\*

Detector type Light yield,

138 New Insights on Gamma Rays

photon/MeV

Effective atomic number is calculated by [40].

conducting neutron-gamma analysis.

(Tl) crystals of a given thickness [44].

The relatively small light yield of BGO scintillators is compensated by the higher densities and atomic numbers of the composition elements. BGO scintillators have approximately the same efficiency as NaI(Tl), but the interactions of neutrons with BGO elements result in the appearance of gamma lines with energy up to 2.5 MeV, which makes this detector unsuitable for measuring gamma spectra in this range in neutron-gamma analysis [45]. Also, a significant drawback of this type of detector is sensitivity to external temperature [46], but a thermal correction system can compensate for this disadvantage [47].

#### 1.3.2.3. LaBr3(Ce)

Among inorganic scintillators, LaBr3(Ce) demonstrated the best resolution and efficiency. Due to the shortest scintillation decay time, this detector had lower background in the high energy part of the spectra due to the smaller number of pile-ups of low energy photons. The presence in this detector of small quantities of the 138La radioactive isotope produces a 1.47-MeV gamma peak, which is always visible in the gamma spectrum and can be used for calibration purposes [1], but does not significantly impact the neutron-stimulated spectra. This detector has stable gamma ray spectra parameters when properly shielded against direct neutron flux from the neutron source. It is the best candidate for active neutron applications, but the high cost of this detector (7.62 cm · 7.62 cm LaBr3(Ce) costs ~US\$35,000 vs US\$2,000 for a high quality NaI(Tl) of similar size [39]) limits a wider use compared to NaI(Tl) and BGO detectors.

#### 1.3.3. Shielding and construction materials

Direct fast neutron flux on the gamma detector and gamma radiation appearing from neutron interaction with detector nuclei leads to high gamma spectra background. High background increases the minimal detection limit of the measurement system and measurement errors. Shielding use between the neutron generator and gamma detector improves characteristics of the measurement system.

Shielding that is one-layer [48–51] or multilayer [52–54] can be used for this purpose. In most cases, fast neutron shielding consists of two or three components which first slows fast neutrons to thermal energy (moderator), absorbs thermal neutrons (absorber), and then attenuates the gamma rays which are produced by different neutrons-nuclei interactions in the moderator and absorber (attenuator). Light materials like water, heavy water, and polyethylene are usually used as neutron moderators, and boric acid is a possible absorber. Sometimes iron is used in the first layer ahead of the light materials to moderate fast neutrons via inelastic neutron scattering [52, 53]. Borated water or borated polyethylene can serve as a combined moderator and absorber in the first layer of shielding. Lead, tungsten, iron, or other such materials with high atomic mass are used to decrease gamma radiation.

While decreasing background, the shielding material and geometry should allow for the counting of useful signal. This means that the shielding thickness should be reasonable and should not produce gamma lines within the energy range of interest. Additionally, it is important to know the possible high energy gamma lines produced from a particular shielding material since they could interfere with useful gamma lines or give additional continuous background lower energy due to the Compton Effect. Construction materials should have minimal susceptibility to neutron activation by fast or thermal neutrons, issue few gamma rays in the energy range of interest, and have minimum high energy gamma rays that increase system background.

#### 1.3.4. Operational electronics and data acquisition software

Operational electronics and data acquisition software essentially depend on the task and particular method modifications. For example, prompt gamma neutron activation analysis with a radioactive isotope neutron source, a standard gamma detector, and multichannel analyzer (e.g., MCA-1000) with its own software could be used [23]. A complicated custommade experimental setup consisting of standard Ortec or Canberra electronic blocks paired with a pulsed neutron generator and gamma and alpha detectors can be used for dangerous material detection (as described in [55]). A custom-made electronic scheme and data acquisition software could be used in some cases due to the absence of suitable standard equipment (e.g., NaI(Tl) detector with corresponding electronics and ProSpect v0.1.11-vega software from XIA LLC, Hayward, CA; see Ref. [56]).

#### 1.4. Modifications of neutron-gamma analysis

Depending on the area of application, different modifications of neutron-gamma analysis can be used. Detailed descriptions of these methods were presented in Ref. [27]; we briefly list these methods below:


Pulsed Fast/Thermal Neutron Analysis (PFTNA) is the most suitable for soil neutron-gamma analysis [57]. The main difficulty conducting soil neutron-gamma analysis is the overlapping of different gamma lines from soil and measurement system nuclei and processes with the main peak of interest (e.g., soil carbon peak). The PFTNA system makes it possible to separate the gamma ray spectrum due to INS reactions (n,n'γ) from the TNC (n,γ) and DA reaction (e.g., (n,p)) spectra. The moderation and moving neutrons in matter limit the incoming neutron—matter nucleus reaction speed. Approximately 1.5 microseconds are required to moderate 14-MeV neutrons to thermal energy in hydrogenous materials [58], while the lifetime of thermal neutrons can be hundreds of microseconds [59]. Thus, INS reactions will only occur during the microsecond neutron pulse, while TNC processes are running during the neutron pulse and between pulses. One memory address of the data acquisition system records during the neutron pulse, while another memory address acquires data between pulses. This is a technique used with small portable electronic neutron generators (see Table 2). PFTNA employs pulses with a duration of 5–20 microseconds. Microsecond pulse durations significantly reduce PFTNA system cost compared to pulsed methods using nanosecond neutron pulses. The PFTNA system employs pulse frequencies greater than 5 kHz to ensure nearly constant thermal neutron flux for the measurement period [60]. When operating at 10 kHz and a 25% duty cycle neutron pulse, it was demonstrated that net count rates in the individual peaks of the soil elements silicon and oxygen in the TNC spectrum have a steady state between neutron pulses [61]. Thus, at first approximation, the count rate registration of gamma flux, which appears under neutron irradiation of samples, can be accepted.

#### 2. Neutron-gamma technology for soil carbon determination

#### 2.1. Importance of soil carbon determination

material since they could interfere with useful gamma lines or give additional continuous background lower energy due to the Compton Effect. Construction materials should have minimal susceptibility to neutron activation by fast or thermal neutrons, issue few gamma rays in the energy range of interest, and have minimum high energy gamma rays that increase

Operational electronics and data acquisition software essentially depend on the task and particular method modifications. For example, prompt gamma neutron activation analysis with a radioactive isotope neutron source, a standard gamma detector, and multichannel analyzer (e.g., MCA-1000) with its own software could be used [23]. A complicated custommade experimental setup consisting of standard Ortec or Canberra electronic blocks paired with a pulsed neutron generator and gamma and alpha detectors can be used for dangerous material detection (as described in [55]). A custom-made electronic scheme and data acquisition software could be used in some cases due to the absence of suitable standard equipment (e.g., NaI(Tl) detector with corresponding electronics and ProSpect v0.1.11-vega software from

Depending on the area of application, different modifications of neutron-gamma analysis can be used. Detailed descriptions of these methods were presented in Ref. [27]; we briefly list

Pulsed Fast/Thermal Neutron Analysis (PFTNA) is the most suitable for soil neutron-gamma analysis [57]. The main difficulty conducting soil neutron-gamma analysis is the overlapping of different gamma lines from soil and measurement system nuclei and processes with the main peak of interest (e.g., soil carbon peak). The PFTNA system makes it possible to separate the gamma ray spectrum due to INS reactions (n,n'γ) from the TNC (n,γ) and DA reaction (e.g., (n,p)) spectra. The moderation and moving neutrons in matter limit the incoming neutron—matter nucleus reaction speed. Approximately 1.5 microseconds are required to moderate 14-MeV neutrons to thermal energy in hydrogenous materials [58], while the lifetime of thermal neutrons can be hundreds of microseconds [59]. Thus, INS reactions will only occur during the microsecond neutron pulse, while TNC processes are running during the neutron pulse and between pulses. One memory address of the data acquisition system records during the neutron pulse, while another memory address acquires data between pulses. This is a technique used with small portable electronic neutron generators (see Table 2). PFTNA employs pulses with a duration of 5–20 microseconds. Microsecond pulse durations

system background.

140 New Insights on Gamma Rays

1.3.4. Operational electronics and data acquisition software

XIA LLC, Hayward, CA; see Ref. [56]).

these methods below:

1.4. Modifications of neutron-gamma analysis

• PFNA—Pulsed Fast Neutron Analysis

• API—Associated Particle Imaging

• PGNAA—Prompt Gamma Neutron Activation Analysis

• PFNTS—Pulsed Fast Neutron Transmission Spectroscopy

• PFTNA—Pulsed Fast/Thermal Neutron Analysis

Adoption of agricultural land use practices adapted for climate change and mitigation potential depends on agricultural productivity and profitability. Understanding and evaluating the impacts on soil resources will influence the development of sustainable land use practices. A critical component of any soil resource evaluation process is measuring and mapping natural and anthropogenic variations in soil carbon storage. Soil carbon can impact many environmental processes, such as soil carbon sequestration, fertility, erosion, and greenhouse gas fluxes [17–20]. The current "gold standard" of soil carbon determination is based on the dry combustion technique (DCT) [62]. This method is destructive, time-consuming, and laborintensive since it involves collecting extensive field soil core samples and requires lots of sample preparation before complex laboratory analysis can be conducted. Furthermore, DCT soil analysis represents a point measurement in space and time that cannot be confidently extrapolated to field or landscape scales which limits its utility for expansive coverage or longer timescale interpretation. Other techniques include laser-induced breakdown spectroscopy, near- and mid-infrared spectroscopy, diffuse reflectance infrared Fourier transform spectroscopy, and pyrolysis molecular beam mass spectrometry [15].

Soil neutron-activation analysis is a new method with the potential for measuring soil carbon in relatively large volumes without having to take destructive soil samples requiring timeconsuming standard laboratory analysis. This new method is based on measuring the gamma response of soil irradiated with fast neutrons. One modification of this method, PFTNA— Pulsed Fast/Thermal Neutron Analysis, has been shown to provide wide-area monitoring for prolonged periods [15, 53]. The result of measurements using this method gives, as will be demonstrated below, the values of average carbon content in weight percent in the upper soil layer (thickness ~10 cm) of ~1.5 m<sup>2</sup> area centered under the neutron source. The measurement time for each surveyed area is 30–60 minutes.

#### 2.2. Problems with neutron-gamma technology in soil carbon analysis and methods of investigation

#### 2.2.1. Features of soil carbon neutron-gamma analysis

The main purpose of this book chapter is to describe the application of neutron-gamma technology for soil elemental analysis. Common features of this technology were described earlier in the introduction. The following aspects of neutron-stimulated gamma ray analysis will be covered:


#### 2.2.2. Methods of investigation

The methods used for investigating the effects of different factors when applying neutrongamma technology for soil elemental analysis are:


All these methods were used during our investigations. Results from these methods will be discussed and compared with each other.

#### 2.2.3. Monte-Carlo simulation method

MC simulations [63, 64] have been extensively used to solve various problems. For example, MC simulations are capable of estimating the neutron flux passing through materials and their energy loss in these materials, determining the energy distribution of emerging neutrons [65], calculating the optimal thickness of shielding [66, 67] and moderator [68], and reproducing the characteristic neutron-induced gamma-ray spectra of different materials [69–73].

An MC simulation model of soil neutron-gamma analysis should consist of two major components—the measurement system and a soil model. The modeled measurement system should mimic the experimental measurement system. The system could have neutron sources (isotropic source with energies that match the experimental setup), detector, and shielding (if required). The MC simulation soil model can be viewed as a three-phase system (solid, liquid, and gaseous phases) [74]. Based on calculation objectives, the soil model may be simplified if all soil parameters critical to the MC simulation are met. Our research used the approach of other researchers [74, 75] where the soil model was constructed as a compact medium with known elemental composition and density depth profiles.

The MC simulation describes randomly issued neutron transportation which includes all interactions with soil components until reaching the simulation volume boundary or exhausting its kinetic energy and disappearing due to an interaction. Some neutron-nuclei interactions result in the appearance of gamma rays which move through and interact with soil components. These interactions cause gamma quanta to disappear as they lose energy; however, some will propagate through the soil and be counted by the detector. The simulated gamma spectrum represents the relationship of the gamma count versus energy. The spectrum shape (number of peaks, their intensity) will be influenced by soil properties. The variation of modeled soil properties and MC simulation of the gamma spectra makes it possible to detect the effect of different soil parameters on the shape of the spectra. Note that the MC simulation gave results that were very close to real data.

#### 2.3. Mobile system for soil carbon determination

• mobile neutron-gamma technology systems for soil carbon content determination;

directly and proportionally dependent on the net carbon signal;

• factors impacting gamma response intensity in quantitative soil analysis.

extracting the net soil carbon signal;

gamma technology for soil elemental analysis are:

discussed and compared with each other.

2.2.3. Monte-Carlo simulation method

meter in volume and weigh around a metric ton.

This method gives useful semi-quantitative results.

simulation results are very close to experimental findings.

chemical analysis;

142 New Insights on Gamma Rays

2.2.2. Methods of investigation

• procedures for measuring the gamma response of neutron-irradiated soil (raw data) and

• soil carbon depth distribution and the particular soil carbon characteristics that are

• comparison of neutron-gamma field measurements of soil carbon content to traditional

The methods used for investigating the effects of different factors when applying neutron-

• Experimental design. Soils being experimentally measured should be around a cubic

• Deterministic modeling. This method involves solution of integral or differential equations that describe the dependence of behavioral characteristics of the system in question in terms of spatial or time coordinates. This method was used in cases of simple shapes and sample properties (e.g., uniform distribution of elements within the sample volume).

• Monte-Carlo (MC) simulation. The gamma response spectra from modeled soil samples irradiated by neutrons are a very effective method to determine the effect of different factors on the neutron-gamma measurement. An MC simulation model of any sample shape, shielding and detector configuration, or measurement geometry is applicable. MC

All these methods were used during our investigations. Results from these methods will be

MC simulations [63, 64] have been extensively used to solve various problems. For example, MC simulations are capable of estimating the neutron flux passing through materials and their energy loss in these materials, determining the energy distribution of emerging neutrons [65], calculating the optimal thickness of shielding [66, 67] and moderator [68], and reproducing the

An MC simulation model of soil neutron-gamma analysis should consist of two major components—the measurement system and a soil model. The modeled measurement system should mimic the experimental measurement system. The system could have neutron sources (isotropic source with energies that match the experimental setup), detector, and shielding (if required). The MC simulation soil model can be viewed as a three-phase system (solid, liquid,

characteristic neutron-induced gamma-ray spectra of different materials [69–73].

As previously described [56], our PFTNA system was mounted on a platform that could be transported by tractors or all-terrain vehicles over various field terrains. The dimensions of our mobile platform were 75 cm · 23 cm · 95 cm and weighs ~300 kg. While the primary construction material was aluminum, the iron shielding contributed more weight. Previous findings [53, 76] were used as a basis for the current construction and electronic system requirements. Our PFTNA system had three separate construction blocks (Figure 4). Components of the first block were an MP320 pulsed neutron generator (Thermo Fisher Scientific, Colorado Springs, CO), an R2D-410 neutron detector (Bridgeport Instruments, LLC, Austin, TX), and a power system (Figure 4a, d). The neutron generator has a pulsed output of 107 –108 n s<sup>1</sup> (depending on parameter settings) and neutron energy of 14 MeV. Components of the PFTNA power system were four DC105-12 batteries (12 V, 105 Ah), a DC-AC inverter (CGL 600Wseries; Nova Electric, Bergenfield, NJ), and a Quad Pro Charger model PS4 (PRO Charging Systems, LLC, LaVergne, TN). The first block also contained water, iron and boric acid shielding for isolating the detector from the neutron beam and focusing the beam on the soil area of interest. The second block had the gamma ray measuring equipment (Figure 4b, e) and contained three 12.7 cm · 12.7 cm · 15.2 cm scintillation NaI(Tl) detectors (Scionix USA, Orlando, FL) with corresponding XIA LLC electronics (XIA LLC, Hayward, CA). For equipment operation, the third block (Figure 4c) housed a laptop computer for controlling the neutron generator, detectors, and data acquisition system ProSpect 0.1 (XIA LLC) (Figure 4c).

In our applied PFTNA technique, gamma rays emitted by soil chemical elements under pulsed neutron irradiation were divided into two groups: emissions during the neutron pulse due to INS and thermo-neutron capture (TNC) and emissions between neutron pulses due to TNC reaction. Delay gamma rays (i.e., caused by neutron activation reactions) are also captured in these spectra. The two concurrent gamma spectra from each PFTNA measurement (i.e., INS +TNC and TNC spectra) were treated together. Spectra acquisition from the three gamma

Figure 4. Overview of the PFTNA sytem: (a) neutron generator, neutron detector, and power system; (b) three NaI (Tl) detectors; (c) equipment operation; (d) general view of A showing individual components; and (e) close-up view of the gamma detectors [56].

detectors can be performed in two separate ways. In the first, analog signals from the detectors go to a summing amplifier for processing by a digital multichannel analyzer [77]. In the second, each detector has a dedicated analog-digital converter for spectra acquisition which can be summarized after correction for energy calibration instability [76, 78]. Our testing showed improved resolution from the second method which was therefore adopted for use in our PFTNA. For autonomous operation under field conditions, we developed a mobile power system for reliable equipment operation over extended periods of time. In this mobile PFTNA system, the neutron generator, neutron and gamma detectors, and laptop computer were all powered by four batteries via a power inverter. This inverter transformed 12 VDC battery power to 110 VAC and could operate with input voltages between 10.9 and 14.7 V.

#### 2.4. Raw data acquisition

Two gamma spectra are acquired with our PFTNA measurements: (1) inelastic neutron scattering (INS) spectra acquired during the neutron pulse and (2) thermo-neutron capture (TNC) spectra acquired between neutron pulses. Typical INS and TNC experimental gamma spectra from soil (raw spectra) are shown in Figure 5 (top and bottom lines, respectively). Each spectrum has a background spectrum and lines due to gamma emission from neutronirradiated soil elements. The main gamma peak of interest has a centroid at 4.45 MeV in the INS spectrum. This peak may be due to neutron interactions with carbon nuclei and the interference of gamma lines from other nuclei. The oxygen peak (6.13 MeV) and the pair production peak (0.511 MeV) are used as reference points for spectral calibration. The INS spectra consist of gamma rays appearing from inelastic neutron scattering, thermal neutron capture, and delay activation of nuclei (samples and system construction materials). The TNC spectra consist of gamma rays from all of the above-listed processes except the INS process.

Figure 5. Raw experimental soil gamma spectra [56].

#### 2.5. Extracting the "Net INS Spectra"

detectors can be performed in two separate ways. In the first, analog signals from the detectors go to a summing amplifier for processing by a digital multichannel analyzer [77]. In the second, each detector has a dedicated analog-digital converter for spectra acquisition which can be summarized after correction for energy calibration instability [76, 78]. Our testing showed improved resolution from the second method which was therefore adopted for use in our PFTNA. For autonomous operation under field conditions, we developed a mobile power system for reliable equipment operation over extended periods of time. In this mobile PFTNA system, the neutron generator, neutron and gamma detectors, and laptop computer were all powered by four batteries via a power inverter. This inverter transformed 12 VDC battery

Figure 4. Overview of the PFTNA sytem: (a) neutron generator, neutron detector, and power system; (b) three NaI (Tl) detectors; (c) equipment operation; (d) general view of A showing individual components; and (e) close-up view of the

D

Two gamma spectra are acquired with our PFTNA measurements: (1) inelastic neutron scattering (INS) spectra acquired during the neutron pulse and (2) thermo-neutron capture (TNC) spectra acquired between neutron pulses. Typical INS and TNC experimental gamma spectra from soil (raw spectra) are shown in Figure 5 (top and bottom lines, respectively). Each spectrum has a background spectrum and lines due to gamma emission from neutronirradiated soil elements. The main gamma peak of interest has a centroid at 4.45 MeV in the INS spectrum. This peak may be due to neutron interactions with carbon nuclei and the interference of gamma lines from other nuclei. The oxygen peak (6.13 MeV) and the pair production peak (0.511 MeV) are used as reference points for spectral calibration. The INS spectra consist of gamma rays appearing from inelastic neutron scattering, thermal neutron

power to 110 VAC and could operate with input voltages between 10.9 and 14.7 V.

2.4. Raw data acquisition

gamma detectors [56].

144 New Insights on Gamma Rays

E

The first step in data processing is extracting the "net INS spectra" from raw data. The acquisition time of INS spectra is the duty cycle of neutron pulses multiplied by measurement clock time (minus dead time of multichannel analyzer), while the acquisition time of TNC spectra is one minus the duty cycle of neutron pulses multiplied by time of measurement (minus dead time of multichannel analyzer). Information on acquisition times is made available by the data acquisition software. Thus, spectra can be represented in counts per second (cps). As a first approximation, the number of INS events in some sample volume at some time moment is proportional to the number of fast neutrons in this volume, while the number of TNC events in some sample volume at some time moment is proportional to the number of thermal neutrons in this volume. In the first approximation, the time dependence of the number of fast neutrons nf(t) can be estimated according to the equation:

$$\frac{dn\_f(t)}{dt} = N(t) - \frac{n\_f(t)}{\tau\_f} \tag{2}$$

where t is a time, N(t) is the neutron flux to the sample from neutron sources, s�<sup>1</sup> , τ<sup>f</sup> is the fast neutron moderation time, s. The fast neutrons convert to thermal neutrons at moderation. The time dependence of the thermal neutron number nth(t) can be estimated as:

$$\frac{d n\_{\hbar t}(t)}{dt} = \frac{n\_f(t)}{\tau\_f} - \frac{n\_{\hbar t}(t)}{\tau\_{\hbar t}} \tag{3}$$

where τth is the lifetime of thermal neutrons. As was discussed earlier (see Section 1.4), the fast 14-MeV neutron thermalization time can be accepted as equal to 1.5 microseconds, while the thermal neutron lifetime can be equal to ~1000 microseconds. The pulse neutron flux (in neutrons per microsecond) with time can be described by the equation:

$$N(t) = \begin{cases} 40 \text{ if } floor \left(\frac{t}{200}\right) \le \frac{t}{200} < floor \left(\frac{t}{200}\right) + 0.25\\ 0 \text{ otherwise} \end{cases} \tag{4}$$

if the neutron flux is 10<sup>7</sup> neutrons per second, frequency is 5000 Hz (pulse time is 200 microseconds), and duty cycle is 0.25 (these pulse neutron generator working regime parameters are for PFTNA of soil).

Solutions for these simple model equations are presented in Figure 6. As can be seen, in the frame of this model the time dependence of fast neutron numbers in the sample practically coincides with neutron flux time dependence (Figure 6a,b), while the time dependence of the thermal neutron is saw-shaped (Figure 6c). If the average value of this "saw" increases at the beginning, the average value reaches a constant value after more than 5000 microseconds (Figure 6d). When the "saw" reaches a constant value, the increase in thermal neutrons during the neutron pulse is practically linear with time, and the decrease in thermal neutrons between pulses is also linear (see Figure 6c). For this reason, the average value of TNC events and consequently the average TNC gamma flux during the neutron pulses is equal to the average value of TNC events and average TNC gamma flux between the neutron pulses. Hence it is possible to accept that the TNC spectra intensity between pulses is approximately the same as the TNC spectra intensity during pulses (in cps per channel). Based on this consideration, the "net INS spectra" can be restored with channel-by-channel subtraction of the TNC spectra from the INS spectra (both expressed in cps).

#### 2.6. Measurement system background signal

Net INS spectrum represents the gamma rays appearing due to inelastic neutron scattering in both the sample and PFTNA system construction materials. The spectrum due to INS of the system construction materials is the background signal of the measurement system. To measure this background signal, the system has to be spatially removed from large objects (e.g., ground, floor, walls, building ceilings). To achieve this, the PFTNA system could be raised above the ground and away from buildings and large objects by using a crane (Figure 7). The measured INS and TNC spectra at different heights above the ground are shown in Figure 8; "net-INS spectra" (difference between INS and TNC spectra, both in cps) are shown in Figure 9. The peaks in these spectra can be attributed solely to INS processes. Intensities can be evaluated to determine the height at which the signal remained uniform with no change. This "no change" signal is considered to be the net INS system background spectrum.

dnthðtÞ

neutrons per microsecond) with time can be described by the equation:

200 ≤ t

<sup>N</sup>ðtÞ ¼ <sup>40</sup> if f loor <sup>t</sup>

from the INS spectra (both expressed in cps).

2.6. Measurement system background signal

for PFTNA of soil).

146 New Insights on Gamma Rays

spectrum.

0 otherwise

dt <sup>¼</sup> nfðt<sup>Þ</sup> τf

where τth is the lifetime of thermal neutrons. As was discussed earlier (see Section 1.4), the fast 14-MeV neutron thermalization time can be accepted as equal to 1.5 microseconds, while the thermal neutron lifetime can be equal to ~1000 microseconds. The pulse neutron flux (in

if the neutron flux is 10<sup>7</sup> neutrons per second, frequency is 5000 Hz (pulse time is 200 microseconds), and duty cycle is 0.25 (these pulse neutron generator working regime parameters are

Solutions for these simple model equations are presented in Figure 6. As can be seen, in the frame of this model the time dependence of fast neutron numbers in the sample practically coincides with neutron flux time dependence (Figure 6a,b), while the time dependence of the thermal neutron is saw-shaped (Figure 6c). If the average value of this "saw" increases at the beginning, the average value reaches a constant value after more than 5000 microseconds (Figure 6d). When the "saw" reaches a constant value, the increase in thermal neutrons during the neutron pulse is practically linear with time, and the decrease in thermal neutrons between pulses is also linear (see Figure 6c). For this reason, the average value of TNC events and consequently the average TNC gamma flux during the neutron pulses is equal to the average value of TNC events and average TNC gamma flux between the neutron pulses. Hence it is possible to accept that the TNC spectra intensity between pulses is approximately the same as the TNC spectra intensity during pulses (in cps per channel). Based on this consideration, the "net INS spectra" can be restored with channel-by-channel subtraction of the TNC spectra

Net INS spectrum represents the gamma rays appearing due to inelastic neutron scattering in both the sample and PFTNA system construction materials. The spectrum due to INS of the system construction materials is the background signal of the measurement system. To measure this background signal, the system has to be spatially removed from large objects (e.g., ground, floor, walls, building ceilings). To achieve this, the PFTNA system could be raised above the ground and away from buildings and large objects by using a crane (Figure 7). The measured INS and TNC spectra at different heights above the ground are shown in Figure 8; "net-INS spectra" (difference between INS and TNC spectra, both in cps) are shown in Figure 9. The peaks in these spectra can be attributed solely to INS processes. Intensities can be evaluated to determine the height at which the signal remained uniform with no change. This "no change" signal is considered to be the net INS system background

� nthðt<sup>Þ</sup> τth

<sup>200</sup> <sup>&</sup>lt; f loor <sup>t</sup>

200 

þ 0:25

ð3Þ

, ð4Þ

Figure 6. Time dependence of the neutron flux (a), number of fast neutrons in a sample nf(t) (b), number of thermal neutrons in a sample nth(t) (c), and time dependency of the number of thermal neutron in a sample at a time more less than 6000 microseconds (d).

Figure 7. The PFTNA system background measurements (up to 6.7 m above the ground) [79].

The behavior of peak areas with centroids at 1.78 MeV ("silicon peak," 28Si), 4.45 MeV ("carbon peak"), and 6.13 MeV ("oxygen peak," 16O) acquired from the net INS spectra are shown in Figure 10. As shown in Figures 8–10, some peaks in the spectra decrease and fully disappear with increasing height (e.g., peaks with centroids at 4.95 and 4.44 MeV in the TNC spectra), while other peaks decrease and reach constant values as height increases. Starting at ~4.5 m height, minimal spectral changes are detected. At this height, the measurement system

Figure 8. INS and TNC spectra measured by the PFTNA system at different heights above the ground [79].

Figure 9. (a) Measurement system net INS spectra (difference between INS and TNC spectra, both in cps) at different heights above the ground; (b) fragment of the net INS spectra around 1.78 MeV; and (c) fragment of the net INS spectra around 4.43 MeV [79].

The behavior of peak areas with centroids at 1.78 MeV ("silicon peak," 28Si), 4.45 MeV ("carbon peak"), and 6.13 MeV ("oxygen peak," 16O) acquired from the net INS spectra are shown in Figure 10. As shown in Figures 8–10, some peaks in the spectra decrease and fully disappear with increasing height (e.g., peaks with centroids at 4.95 and 4.44 MeV in the TNC spectra), while other peaks decrease and reach constant values as height increases. Starting at ~4.5 m height, minimal spectral changes are detected. At this height, the measurement system

Figure 7. The PFTNA system background measurements (up to 6.7 m above the ground) [79].

148 New Insights on Gamma Rays

Figure 10. Dependencies of peaks areas with centroids at 1.78, 4.43, and 6.13 MeV in the net INS spectra for measurement system with changing heights above the ground [79].

is far enough away from the ground (and other large objects) that the gamma responses from these objects are negligible compared with the gamma responses from the measurement system construction materials. The net INS spectrum acquired at a height more than 4.5 m could be used as the system background spectrum.

#### 2.7. "Soil Net INS Spectra" and "Soil Carbon Net Peak"

The "soil net INS spectrum" can be obtained from the results of soil measurements and the system background spectra. For this, "the system background net INS spectrum" should be subtracted (channel by channel) from the soil net INS spectrum received from the raw INS and TNC spectra (all spectra should be in cps). The "soil net INS spectrum" consists of gamma rays which appear due to inelastic neutron scattering of fast neutrons on soil nuclei.

Main peak of interest in "the soil net INC-spectra" is the peak with a centroid at 4.45 MeV. Analysis showed (see Figure 11) that this peak can consist of the soil carbon peak with centroid at 4.44 MeV, soil silicon cascade transition peak with centroid at 4.50 MeV; possibly the carbon peak with centroid at 4.44 MeV has contribution from excited carbon nuclei as a result of INS on other soil nuclei (e.g., due to 16O (n,n'α) 12C\* !12C + <sup>γ</sup>(4.44 MeV) reaction [80]). Silicon 28Si nuclei turn to different excited states due to INS on silicon nuclei. The relaxation of excited silicon passes through the first exited state with energy 1.78 MeV, and the transition to ground state is accompanied by issued gamma rays with energy close to 1.78 MeV (i.e., "soil net silicon peak"). The relaxation of the 6.28 MeV silicon excited state pass to ground state through the first excited state and is accompanied by gamma rays with energy close to 4.50 MeV (6.28 � 1.78 MeV); that is the "silicon cascade transition peak" [22]. This peak can be a part of the 4.45 MeV

Figure 11. Composition of the 4.45-MeV peak in the soil net INS spectrum.

peak in the "soil net INS spectra." The theoretical calculation of the 4.50 to 1.78 MeV gamma ray intensity ratio (i.e., "cascade transition coefficient") gives a value of 0.0547 [81].

#### 2.8. Defining "Soil Carbon Net Peak Area" for a uniform carbon depth profile

#### 2.8.1. Measured gamma spectra of sand-carbon pits

is far enough away from the ground (and other large objects) that the gamma responses from these objects are negligible compared with the gamma responses from the measurement system construction materials. The net INS spectrum acquired at a height more than 4.5 m

Figure 10. Dependencies of peaks areas with centroids at 1.78, 4.43, and 6.13 MeV in the net INS spectra for measurement

The "soil net INS spectrum" can be obtained from the results of soil measurements and the system background spectra. For this, "the system background net INS spectrum" should be subtracted (channel by channel) from the soil net INS spectrum received from the raw INS and TNC spectra (all spectra should be in cps). The "soil net INS spectrum" consists of gamma rays

Main peak of interest in "the soil net INC-spectra" is the peak with a centroid at 4.45 MeV. Analysis showed (see Figure 11) that this peak can consist of the soil carbon peak with centroid at 4.44 MeV, soil silicon cascade transition peak with centroid at 4.50 MeV; possibly the carbon peak with centroid at 4.44 MeV has contribution from excited carbon nuclei as a result of INS

12C\*

nuclei turn to different excited states due to INS on silicon nuclei. The relaxation of excited silicon passes through the first exited state with energy 1.78 MeV, and the transition to ground state is accompanied by issued gamma rays with energy close to 1.78 MeV (i.e., "soil net silicon peak"). The relaxation of the 6.28 MeV silicon excited state pass to ground state through the first excited state and is accompanied by gamma rays with energy close to 4.50 MeV (6.28 � 1.78 MeV); that is the "silicon cascade transition peak" [22]. This peak can be a part of the 4.45 MeV

!12C + <sup>γ</sup>(4.44 MeV) reaction [80]). Silicon 28Si

which appear due to inelastic neutron scattering of fast neutrons on soil nuclei.

could be used as the system background spectrum.

system with changing heights above the ground [79].

150 New Insights on Gamma Rays

on other soil nuclei (e.g., due to 16O (n,n'α)

2.7. "Soil Net INS Spectra" and "Soil Carbon Net Peak"

Measurements of INS and TNC spectra using the PFTNA system were performed over 1.5 m · 1.5 m · 0.6 m pits filled with uniform sand-carbon mixtures that had carbon contents of 0, 2.5, 5, and 10 w%. The measurement system was placed over each pit such that the neutron source was situated over the geometric center of each pit. The "soil net INS spectra" were calculated for each pit, taking into account the system background spectra as described above. The experimental "net INS spectra" for pits are shown in Figures 12 and 13.

#### 2.8.2. Monte-Carlo simulated gamma spectra of sand-carbon pits

MC simulations of gamma spectra from pits (1.5 m · 1.5 m · 0.6 m) with different sandcarbon mixtures using model geometry very similar to experimental system geometry were evaluated. The soil models are represented as compact media with above-mentioned dimensions and uniform SiO2+C composition densities

$$d\_{\rm mix} = \frac{1.7 \cdot 0.52 \cdot 100}{\rm Cu\% \cdot 1.7 + (100 - \rm Cu\%) \cdot 0.52} \tag{5}$$

where 1.7 is the sand bulk density (g cm�<sup>3</sup> ); 0.52 is the coconut shell bulk density used in the pits as carbon (g cm�<sup>3</sup> ), and Cw% is the carbon content of the mixture in weight percent. The

Figure 12. Simulated and measured 14-MeV neutron-stimulated net INS gamma spectra of sand-carbon mixtures (0, 2.5, 5, 10 w% C) in 1.5 m · 1.5 m · 0.6 m pits [79].

Figure 13. Fragment of simulated and measured 14-MeV neutron-stimulated net INS gamma spectra of sand-carbon mixtures (0, 2.5, 5, 10 w% C) in 1.5 m · 1.5 m · 0.6 m pits [79].

simulation model consisted of a point isotropic neutron source, gamma detector, and shielding similar to the real measurement system. The distance between the source and detector (35 cm), height of the model system above the ground, and number and type of detectors (three NaI(Tl) 12.7 cm · 12.7 cm · 15.3 cm) were the same as in the experimental system. The Geant4 tool kit [82] version G4.10.01p.01 [83] was used to conduct the MC simulations for this and other research issues. A conventional laptop with a multicore processor and high performance computing cluster (Auburn University Samuel Ginn College of Engineering vSMP HPCC consists of 512 cores @ 2.80 GHz X5560, 1.536TB shared memory, and 20.48TB raw internal storage) were used for calculation in the multithread mode. Note that for accuracy of the simulated spectra to approximately equal the experimental accuracy, 109 simulation events should be performed. Due to the large number of simulation events, the simulation time for each spectrum was several dozen hours. Our simulation used the neutron cross section JENDL4.0 database rather than the default database (G4NDL4.5) due to the JENDL4.0 simulated spectra and the experimental spectra being more similar. From Geant4 toolkit, we used the QGSP BIC HP and QGSP BERT HP physics lists (Reference Physics Lists, 2014). Both lists had high precision models for neutron transport below 20 MeV and gave the same simulation results. The change in detector energy resolution was taken into account as ~1.142 ffiffiffiffiffi Eγ p (E<sup>γ</sup> is the gamma quanta energy, keV) during simulation. This type of energy resolution dependence for gamma detectors is known [84], and the multiplier 1.142 was determined by matching the width of the simulated 137Cs peak to that in the experimental spectra. The detector efficiency dependence with energy was not accounted for since this change would be minor due to the large NaI crystal sizes in the 1–10 MeV energy range [44]. Only INS spectra were simulated; other processes (like thermal neutron capture) in the simulation code were deactivated. A computer screenshot of the simulation model is shown in Figure 14.

#### 2.8.3. MC simulated system background "INS spectra"

Figure 12. Simulated and measured 14-MeV neutron-stimulated net INS gamma spectra of sand-carbon mixtures (0, 2.5,

Figure 13. Fragment of simulated and measured 14-MeV neutron-stimulated net INS gamma spectra of sand-carbon

5, 10 w% C) in 1.5 m · 1.5 m · 0.6 m pits [79].

152 New Insights on Gamma Rays

mixtures (0, 2.5, 5, 10 w% C) in 1.5 m · 1.5 m · 0.6 m pits [79].

For determination of "pit net INS spectra," the system background "INS spectra" should first be simulated. In this simulation, the measurement model geometry and system components (detectors, shielding, sizes) were the same, but the pit model was absent. The "pit net INS spectra" are represented with channel-by-channel differences (simulation channel width is 10 keV) between "pit INS spectra" and system background "INS spectra."

The effect of system background on the simulated spectra is demonstrated as follows. Figure 15 represents simulated "INS spectra" of the SiO2+5w%C pit measured by a system consisting of a neutron source, sodium iodide detector, and different shielding. The system background spectra with different shielding are also shown in this plot. The "pit net INS spectra" (difference between "pit INS spectra" and "background INS spectra") are presented in Figure 16. As can be seen, the shape of each "pit INS spectra" measured by the system with different shielding is also different. Similar variations were also seen in "background INS spectra" for different shielding, but the "pit net INS spectra" were the same in all cases (Figure 16). Although these results may appear trivial, this example demonstrates that parts of system background in the raw spectra can be significant and should be taken into account by subtraction in quantitative analysis. Similar subtractions should be performed in experimental measurements.

Figure 14. The MC simulation model (Geant4) for measurement of gamma response from a sand-carbon pit under neutron irradiation.

Figure 15. Simulated Geant4 gamma spectra of Pit SiO2 + 5%C and background for system with different shielding.

Figure 16. Simulated Geant4 "net pit INS spectra" of Pit SiO2 + 5%C for system with different shielding.

#### 2.8.4. Dependence of "Soil Carbon Net Peak" area versus pit carbon content

Sand-Carbon Pit 150 cm x 150 cm x 60 cm

Figure 14. The MC simulation model (Geant4) for measurement of gamma response from a sand-carbon pit under

Figure 15. Simulated Geant4 gamma spectra of Pit SiO2 + 5%C and background for system with different shielding.

Neutron Source

154 New Insights on Gamma Rays

neutron irradiation.

Gamma rays

Neutrons

Gamma detectors

Shielding:

Boric Acid Water

Fe

The MC simulated and measured "pit net INS spectra" for pits with different carbon contents are shown in Figure 12. As can be seen, the simulated and measured spectra are similar. The simplicity of the model combined with not accounting for the detector efficiency with energy may help explain some differences between measured and simulated spectra. Despite the insignificant discrepancies between measured and simulated spectra, the main features (i.e., position and relative intensity of pair production; and silicon, oxygen, and carbon peaks) are approximately the same, and both were used in our analysis.

Assuming that the "soil carbon net peak" area value can be determined as "4.45 MeV net peak" area minus the "soil silicon net peak" area multiplied by some coefficient f, then the "4.45 MeV net peak" consists of only the "soil carbon net peak" and "silicon cascade transition peak," with the addition of other gamma rays being negligible. In this case, the dependence of the "soil carbon net peak" area versus carbon content should pass through the "zero-zero" point where the value of this coefficient equals the "cascade transition coefficient."

To define the dependence of the "soil carbon net peak" area versus carbon content for both the simulated and measured spectra, the "4.45 MeV net peak" area and "soil silicon net peak" area are determined in both the experimental and simulated spectra. In this case, spectral peaks of interest were approximated by one or two Gaussian shape curves using Igor Pro standard software [85] to determine the area beneath the curve. It is important to note that since peak fitting by summing two Gaussians gives approximately the same value for different component parameters, this sum was used in the analysis rather than the area of the components. An example of the simulated gamma spectra with fitted peaks with a centroid at 1.78 MeV ("soil silicon net peak") and 4.45 MeV ("4.45 MeV net peak") by Gaussian shape curves is shown in Figure 17.

The "soil carbon net peak area" in the i-th spectrum was denoted as Ccorri. The "soil silicon net peak" area in the i-th spectrum was denoted as SSii, "4.45 MeV net peak" area in the i-th spectrum as SCi, and carbon content in the i-th mixture as Conti. The assumption was that Ccorri can be calculated as (SCi fSSii); Ccorri was considered to be directly proportional to the

Figure 17. An example of the simulated gamma spectra for the model soil sample and designations of the peak areas used in the calculations: points—simulated data, solid lines—approximation by one (1.78 MeV, "net soil silicon peak") or sum of two Gaussians (4.45 MeV, "net 4.45 MeV peak"), dotted lines—peak components, and dashed lines—background.

carbon content of the mixture (kConti) with f and k being the coefficients (these designations are shown in Figure 17 for clarity). Using SCi and SSii data, the values of f and k can be determined by minimizing the expression

$$\sum\_{i} (\mathbf{SC}\_{i} - f \cdot \mathbf{SSi}\_{i} - k \cdot \mathbf{Cont}\_{i})^{2} \to \min \tag{6}$$

The f and k values were found by equating the derivatives of this sum with respect to f and k set to zero. These calculations were performed using the standard mathematical software, MathCAD (Parametric Technology Corporation, 2013).

by summing two Gaussians gives approximately the same value for different component parameters, this sum was used in the analysis rather than the area of the components. An example of the simulated gamma spectra with fitted peaks with a centroid at 1.78 MeV ("soil silicon net peak")

The "soil carbon net peak area" in the i-th spectrum was denoted as Ccorri. The "soil silicon net peak" area in the i-th spectrum was denoted as SSii, "4.45 MeV net peak" area in the i-th spectrum as SCi, and carbon content in the i-th mixture as Conti. The assumption was that Ccorri can be calculated as (SCi fSSii); Ccorri was considered to be directly proportional to the

carbon content of the mixture (kConti) with f and k being the coefficients (these designations are shown in Figure 17 for clarity). Using SCi and SSii data, the values of f and k can be

Figure 17. An example of the simulated gamma spectra for the model soil sample and designations of the peak areas used in the calculations: points—simulated data, solid lines—approximation by one (1.78 MeV, "net soil silicon peak") or sum of two Gaussians (4.45 MeV, "net 4.45 MeV peak"), dotted lines—peak components, and dashed lines—background.

determined by minimizing the expression

and 4.45 MeV ("4.45 MeV net peak") by Gaussian shape curves is shown in Figure 17.

156 New Insights on Gamma Rays

The dependencies between the "4.45 MeV net peak" area, "soil silicon net peak" area, and "soil carbon net peak" area Ccorr with carbon content from simulated and measured spectra are presented in Figure 18. As can be seen, the dependencies in both cases are similar to each other and pass through the "zero-zero" point. In addition, the values of the coefficient f from data processing of both the experimental and simulated spectra are very close (0.054 and 0.058, respectively). Values of this parameter (i.e., coefficient of the cascade transition for 28Si nuclei) are similar to earlier published values [81, 86]. Thus, it is possible to define the "soil carbon net peak" area (from the "soil net INS spectra") as "4.45 MeV net peak" area minus "soil silicon net peak" area multiplied by some coefficient f, where f is the "cascade transition coefficient" equal

Figure 18. Dependencies of the "4.45 MeV net peak" area SC, "soil silicon net peak" area SSi, and "soil carbon net peak area" Ccorr with carbon content from the simulated and measured spectra of sand-carbon mixtures [79].

to 0.0547 without taking into account the effect of other INS processes like 16O (n,n'α) 12C\* !12C + <sup>γ</sup> (4.44 MeV).

#### 2.9. Parameter selection for soil carbon characterization

The carbon gamma signal intensity ("soil carbon net peak" area) measured by the gamma detector is dependent on neutron flux intensity and soil conditions (density and element content), but the gamma signal intensity can be strongly influenced by the distribution of carbon within the soil depth profile. Neutron penetration depth and gamma flux attenuation are determined by soil properties. The distribution of soil carbon with depth is usually nonuniform (i.e., carbon level decreases as depth increases) and by first approximation can be described by exponential law [15]. The parameters of these distributions vary from site to site [56]. For this reason, correlations between the carbon peak intensity in the gamma spectrum and characterization of soil carbon content parameters are not obvious.

#### 2.9.1. Parameter candidates

The main problem is determining which characteristic of soil carbon content has a direct proportional dependency (even in some approximation) with "soil carbon net peak" area. In general, the average carbon content or integral by some depth can be used to characterize the carbon in some depth layer. The tested candidates were average parameter—average carbon weight percent in some soil layer (AvgCw%(h), where h is the layer thickness) and integral parameter—grams carbon per square centimeter of soil surface in a layer of some thickness (SD(h), surface density). These parameters can be calculated as:

$$Avg \mathsf{C}w^{\otimes}\%(h) = \frac{1}{h} \int\_0^h W\%(b)db\tag{7}$$

$$SD(h) = \int\_0^h W\%(b) \cdot d(b)db\tag{8}$$

where W%(b) is carbon weight percent at depth b and d(b) is the soil density at depth b. Note that another possible characteristic will be proportional to one of these characteristics.

#### 2.9.2. "Surface Density in 30 cm" parameter

Ref. [15] reported that the value of the carbon signal in INS spectra was connected to the surface density of carbon in a 30-cm layer. Figure 19 shows three carbon depth profiles in modeled sand-carbon mixtures for which the values of SD(30) are all equal to 2.29 gC cm�<sup>2</sup> . But the fragment of the MC simulated INS spectra around the carbon peak (Figure 20) for these modeled sand-carbon mixtures illustrates that these peaks are quite different despite SD (30) being the same for all mixtures. Thus, the "soil carbon net peak" area is not directly proportional to soil carbon content expressed in carbon surface density at 30 cm; this indicates that some other parameter should be found.

The effect of carbon depth profile and soil elemental content on the gamma spectrum as a whole (particularly for the carbon peak) can be determined from experimental results for soil sites with different carbon depth profiles, and by varying the carbon depth profile

to 0.0547 without taking into account the effect of other INS processes like 16O (n,n'α)

and characterization of soil carbon content parameters are not obvious.

(SD(h), surface density). These parameters can be calculated as:

2.9.2. "Surface Density in 30 cm" parameter

that some other parameter should be found.

AvgCw%ðhÞ ¼ <sup>1</sup>

ðh 0

that another possible characteristic will be proportional to one of these characteristics.

SDðhÞ ¼

The carbon gamma signal intensity ("soil carbon net peak" area) measured by the gamma detector is dependent on neutron flux intensity and soil conditions (density and element content), but the gamma signal intensity can be strongly influenced by the distribution of carbon within the soil depth profile. Neutron penetration depth and gamma flux attenuation are determined by soil properties. The distribution of soil carbon with depth is usually nonuniform (i.e., carbon level decreases as depth increases) and by first approximation can be described by exponential law [15]. The parameters of these distributions vary from site to site [56]. For this reason, correlations between the carbon peak intensity in the gamma spectrum

The main problem is determining which characteristic of soil carbon content has a direct proportional dependency (even in some approximation) with "soil carbon net peak" area. In general, the average carbon content or integral by some depth can be used to characterize the carbon in some depth layer. The tested candidates were average parameter—average carbon weight percent in some soil layer (AvgCw%(h), where h is the layer thickness) and integral parameter—grams carbon per square centimeter of soil surface in a layer of some thickness

> h ðh 0

where W%(b) is carbon weight percent at depth b and d(b) is the soil density at depth b. Note

Ref. [15] reported that the value of the carbon signal in INS spectra was connected to the surface density of carbon in a 30-cm layer. Figure 19 shows three carbon depth profiles in modeled sand-carbon mixtures for which the values of SD(30) are all equal to 2.29 gC cm�<sup>2</sup>

But the fragment of the MC simulated INS spectra around the carbon peak (Figure 20) for these modeled sand-carbon mixtures illustrates that these peaks are quite different despite SD (30) being the same for all mixtures. Thus, the "soil carbon net peak" area is not directly proportional to soil carbon content expressed in carbon surface density at 30 cm; this indicates

The effect of carbon depth profile and soil elemental content on the gamma spectrum as a whole (particularly for the carbon peak) can be determined from experimental results for soil sites with different carbon depth profiles, and by varying the carbon depth profile

W%ðbÞdb ð7Þ

W%ðbÞ � dðbÞdb ð8Þ

2.9. Parameter selection for soil carbon characterization

(4.44 MeV).

158 New Insights on Gamma Rays

2.9.1. Parameter candidates

12C\*

!12C + <sup>γ</sup>

.

Figure 19. Carbon depth profiles in modeled sand-carbon mixtures for which the value of SD(30) = 2.29 gC cm<sup>2</sup> .

Figure 20. Fragment of MC simulated INS spectra (around the carbon peak) of modeled sand-carbon mixtures with carbon depth profiles shown in Figure 19. For all cases SD(30) = 2.29 gC cm<sup>2</sup> .

parameters in the soil model during MC simulations. These measurements and simulations were done to further our understanding of the relationship between INS signals and soil carbon content.

#### 2.10. "Net INS Spectra" for nonuniform carbon depth profile sites

#### 2.10.1. Carbon, soil density, and main element depth profile examples

Figures 21–23 show carbon depth profiles, soil density examples, and main element depth profiles from sampling sites. These carbon depth profiles were from an applied field (AF) located at the Piedmont Research Unit, Camp Hill, AL, USA [41]. Data was from traditional dry combustion chemical analysis of cores collected from the AF sites. Dependencies shown in Figures 21–23 were used to construct the soil model in the simulation. Six artificial carbon depth profiles with extremal shapes (Art1–Art6 in Figure 21) were also used in the simulations.

#### 2.10.2. Measured and simulated net INS spectra for sites with nonuniform carbon depth profile

Raw INS and TNC spectra were collected for each site and replotted in units of "counts per second." Afterwards, "soil net INS spectra" were calculated taking into account the "system background spectra" data (as was described above). For MC simulation, "soil net INS spectra" were calculated by subtracting the previously simulated system background spectra from the "soil INS spectra." Examples of measured and simulated spectra for some of these areas are

Figure 21. Soil carbon depth profiles for different sites (see text for details) [79].

Figure 22. Soil density depth profile for the experimental site [79].

parameters in the soil model during MC simulations. These measurements and simulations were done to further our understanding of the relationship between INS signals and soil

Figures 21–23 show carbon depth profiles, soil density examples, and main element depth profiles from sampling sites. These carbon depth profiles were from an applied field (AF) located at the Piedmont Research Unit, Camp Hill, AL, USA [41]. Data was from traditional dry combustion chemical analysis of cores collected from the AF sites. Dependencies shown in Figures 21–23 were used to construct the soil model in the simulation. Six artificial carbon depth profiles with extremal shapes (Art1–Art6 in Figure 21) were also used in the simulations.

2.10.2. Measured and simulated net INS spectra for sites with nonuniform carbon depth profile

Raw INS and TNC spectra were collected for each site and replotted in units of "counts per second." Afterwards, "soil net INS spectra" were calculated taking into account the "system background spectra" data (as was described above). For MC simulation, "soil net INS spectra" were calculated by subtracting the previously simulated system background spectra from the "soil INS spectra." Examples of measured and simulated spectra for some of these areas are

2.10. "Net INS Spectra" for nonuniform carbon depth profile sites

2.10.1. Carbon, soil density, and main element depth profile examples

Figure 21. Soil carbon depth profiles for different sites (see text for details) [79].

carbon content.

160 New Insights on Gamma Rays

Figure 23. An example depth profile of the main soil elements [79].

shown in Figure 24. As can be observed, the simulated and measured spectra were very similar to each other. The peak areas with centroids at 1.78 and 4.45 MeV were calculated from these spectra using approximation by one or two Gaussian shape curves with Igor Pro standard software [85]. Next, "soil carbon net peak" area was defined using the above described procedure by subtracting the "soil silicon net peak" multiplied by the "cascade transition coefficient" (i.e., 0.0547) from the "4.45 MeV net peak" for each measurement and simulation.

#### 2.10.3. Calibration

The calibration coefficient to calculate the carbon content from the value of "soil carbon net peak" area was determined from the gamma spectra for pits with uniform sand-carbon mixtures. Carbon content can be denoted in units of the average carbon weight percent or surface density. For uniform sand-carbon mixtures, the calibration line "soil carbon net peak" area versus w% will not depend on the thickness, while the calibration line "soil carbon net peak" area versus SD will depend on the given thickness. The carbon characterization parameter should be applied for any carbon depth profile, including uniform distribution. Thus, it should be possible to use the calibration coefficients derived from uniform distribution to calculate the carbon characterization parameter for the spectra of sites with nonuniform carbon depth distribution.

Calibration dependencies for uniform sand-carbon mixtures were also constructed for simulated and experimental spectra. In this case, the coefficients f and kw%, j and kSD, j (j = 1 for measurement, j = 2 for MC simulation) were determined as described above by Eqs. (7) and (8) for both cases, where Conti corresponded to W% or SD(h). For uniform mixtures, AvgCw% does not depend on h. Thus, there is only one set of coefficients f and kw%, j. SD depends on h; therefore, each h has its own set of coefficients, f and kSD,j(h). In the set of coefficients for SD, the coefficient f is the same for each h and approximately the same for f for weight percent. Using the determined coefficients, the dependencies of the "soil carbon net peak" area (Ccorr) in the measured and MC simulated spectra versus carbon weight percent and versus carbon surface density at different thicknesses [SD(h)] in sand-carbon mixtures samples were plotted (Figures 25 and 26). These figures illustrate that the dependencies of Ccorr with W% and with SD(h) are directly proportional within measurement and simulation accuracy limits in all cases.

Figure 24. Measured (a) and MC simulated (b) "net soil INS gamma spectra" of different sites with characteristics shown in Figures 21–23.

Figure 25. Calibration lines plotted from data of MC simulations [79]: (a) "net soil carbon peak" area versus carbon surface density; thickness shown near line; (b) "net soil carbon peak" area versus carbon weight percent.

Figure 26. Calibration lines plotted from measurement data: (a) "net soil carbon peak" area versus carbon surface density; thickness shown near line; (b) "net soil carbon peak" area versus carbon weight percent [79].

#### 2.11. Comparison of PFTNA and chemical analysis data

shown in Figure 24. As can be observed, the simulated and measured spectra were very similar to each other. The peak areas with centroids at 1.78 and 4.45 MeV were calculated from these spectra using approximation by one or two Gaussian shape curves with Igor Pro standard software [85]. Next, "soil carbon net peak" area was defined using the above described procedure by subtracting the "soil silicon net peak" multiplied by the "cascade transition coefficient" (i.e., 0.0547) from the "4.45 MeV net peak" for each measurement and simulation.

The calibration coefficient to calculate the carbon content from the value of "soil carbon net peak" area was determined from the gamma spectra for pits with uniform sand-carbon mixtures. Carbon content can be denoted in units of the average carbon weight percent or surface density. For uniform sand-carbon mixtures, the calibration line "soil carbon net peak" area versus w% will not depend on the thickness, while the calibration line "soil carbon net peak" area versus SD will depend on the given thickness. The carbon characterization parameter should be applied for any carbon depth profile, including uniform distribution. Thus, it should be possible to use the calibration coefficients derived from uniform distribution to calculate the carbon characterization

Calibration dependencies for uniform sand-carbon mixtures were also constructed for simulated and experimental spectra. In this case, the coefficients f and kw%, j and kSD, j (j = 1 for measurement, j = 2 for MC simulation) were determined as described above by Eqs. (7) and (8) for both cases, where Conti corresponded to W% or SD(h). For uniform mixtures, AvgCw% does not depend on h. Thus, there is only one set of coefficients f and kw%, j. SD depends on h; therefore, each h has its own set of coefficients, f and kSD,j(h). In the set of coefficients for SD, the coefficient f is the same for each h and approximately the same for f for weight percent. Using the determined coefficients, the dependencies of the "soil carbon net peak" area (Ccorr) in the measured and MC simulated spectra versus carbon weight percent and versus carbon surface density at different thicknesses [SD(h)] in sand-carbon mixtures samples were plotted (Figures 25 and 26). These figures illustrate that the dependencies of Ccorr with W% and with SD(h) are directly propor-

Figure 24. Measured (a) and MC simulated (b) "net soil INS gamma spectra" of different sites with characteristics shown

parameter for the spectra of sites with nonuniform carbon depth distribution.

tional within measurement and simulation accuracy limits in all cases.

2.10.3. Calibration

162 New Insights on Gamma Rays

in Figures 21–23.

The calculation of AvgCw%(h)DC,i and SD(h)DC,i by chemical analysis (dry combustion) was done for each site to compare with the data received from the INS gamma spectra. Coincidence of values for some parameters received from the net soil INS gamma spectra and from chemical analysis will mean the value of this parameter can be determined from neutrongamma measurements.

#### 2.11.1. Dependence of average values of relative differences with h

For each site, the relative difference between Cw%INS,i,j and SDINSi,j(h) for INS and MC simulation data and AvgCw%(h)DC,i and SD(h)DC,i values from soil chemical analysis data were used to compare these values.

$$\text{Cov}\_{\%,j}(h) = \frac{\text{AvgCw}\%(h)\_{\text{DC},i} - \text{Cw}\%\_{\text{INS},i,j}}{\text{Cw}\_{\%\text{INS},i,j}} \tag{9}$$

$$rSD\_{i,j}(h) = \frac{SD(h)\_{D\mathbb{C},i} - SD\_{\text{INS},i,j}}{SD\_{\text{INS},i,j}} \tag{10}$$

The relative difference values for each site with h were calculated and plotted. The example of rw%i,2ðhÞ for different sites is shown in Figure 27. This relative difference was found to be equal to zero at some layer thickness h for each site. As can be seen in Figure 27, this depth varies around 10 cm in the range of �2 cm for all sites.

The dependence of average values of relative differences for weight percent and for surface densities, ξw%jðhÞ and ξCDjðhÞ for all surveyed sites with h, were calculated as

$$\xi w \% \!/ (h) = \frac{1}{N\_{\!\!\!/}} \sum\_{i} \frac{\text{AvgCw} \% (h)\_{\text{DC},i} - \text{Cw} \%\_{\text{INS},i,j}}{\text{Cw} \%\_{\text{INS},i,j}} \tag{11}$$

$$\xi \mathbb{C} D\_{\rangle}(h) = \frac{1}{N\_{\rangle}} \sum\_{i} \frac{\mathbb{S}D(h)\_{\text{DC},i} - \mathbb{S}D\_{\text{INS},i,j}}{\text{SD}\_{\text{INS},i,j}} \tag{12}$$

where Nj is the number of the sites used in measurements (j = 1) and in MC simulations (j = 2); both demonstrated some form of regularity. These dependencies are shown in Figure 28 for both measurements and MC simulations.

Equality ξw%jðhÞ or ξCDjðhÞ value to zero means that, at this h, the soil carbon characteristics determined from the spectra and from depth distribution are very similar. As one can see, the carbon weight percent derived from the spectra coincides with the average weight percent at a thickness of ~10 cm. Figure 28 shows that the values of surface density from the spectra and from depth profiles differ from each other at any thickness. From these results we conclude

Figure 27. The dependence of the relative difference between Cw%INS,i,j for data received from MC simulation and AvgCw%(h)DC,i values from chemical analysis data with h for sites used for MC simulations [79].

rSDi,jðhÞ ¼

densities, ξw%jðhÞ and ξCDjðhÞ for all surveyed sites with h, were calculated as

Nj X i

Nj X i

<sup>ξ</sup>CDjðhÞ ¼ <sup>1</sup>

around 10 cm in the range of �2 cm for all sites.

164 New Insights on Gamma Rays

both measurements and MC simulations.

<sup>ξ</sup>w%jðhÞ ¼ <sup>1</sup>

SDðhÞDC,i � SDINS,i,j SDINS,i,j

AvgCw%ðhÞDC,i � Cw%INS,i,j Cw%INS,i,j

> SDðhÞDC,i � SDINS,i,j SDINS,i,j

The relative difference values for each site with h were calculated and plotted. The example of rw%i,2ðhÞ for different sites is shown in Figure 27. This relative difference was found to be equal to zero at some layer thickness h for each site. As can be seen in Figure 27, this depth varies

The dependence of average values of relative differences for weight percent and for surface

where Nj is the number of the sites used in measurements (j = 1) and in MC simulations (j = 2); both demonstrated some form of regularity. These dependencies are shown in Figure 28 for

Equality ξw%jðhÞ or ξCDjðhÞ value to zero means that, at this h, the soil carbon characteristics determined from the spectra and from depth distribution are very similar. As one can see, the carbon weight percent derived from the spectra coincides with the average weight percent at a thickness of ~10 cm. Figure 28 shows that the values of surface density from the spectra and from depth profiles differ from each other at any thickness. From these results we conclude

Figure 27. The dependence of the relative difference between Cw%INS,i,j for data received from MC simulation and

AvgCw%(h)DC,i values from chemical analysis data with h for sites used for MC simulations [79].

ð10Þ

ð11Þ

ð12Þ

Figure 28. The dependence of average values of relative differences for weight percent (circles) and for surface densities (triangles) for measurements (a) and for MC simulations (b) with h [79].

that the soil carbon content parameter (based on gamma spectra using uniform carbon-sand mixture calibration data) is the average carbon weight percent for a 10-cm soil layer.

Therefore, INS simulation results (value of Cw%INS,i, <sup>2</sup> ) can be attributed to average carbon weight percent in the soil layer with thickness h. Since different carbon depth profiles (from constant levels to sharp declines) were used in the simulations, the parameter (average carbon weight percent in soil layer with thickness 10 cm) could be assigned to the value determined from any INS gamma spectra.

#### 2.11.2. Average carbon weight percent measured by PFTNA and chemical analysis

Results of carbon content measurements (average weight percent in upper 10-cm soil layer and its standard deviation) are shown in Table 4. Measurements were conducted by two methods (dry combustion and PFTNA). For clarity, the data from the open field at the Camp Hill location are shown in Figure 29. These data demonstrate good agreement between methods, especially for average values over whole plots. It should be noted that the accuracy of the carbon concentration measurement using PFTNA is comparable to measured values when the carbon concentration value is ~1 w% or less. To increase the accuracy of INS measurement at low soil carbon levels, further modification of our system is required. Such modifications would include (i) optimizing the detector's positioning relative to the neutron generator; (ii) increasing the number of detectors; and (iii) optimizing radiation shielding.

Data on soil carbon content can be used in mapping. Two maps of carbon distribution in the upper soil layer on one of the surveyed fields based on neutron-gamma analysis (PFTNA methods, Figure 30a) and chemical analysis (dry combustion, Figure 30b) are shown for comparison. As can be seen, both of these maps are very similar to each other. It should be noted that it took more than 1.5 months to collect the data for carbon content mapping in Figure 30b (dry combustion method), while only 2 working days were required for collecting carbon content data mapped in Figure 30a (PFTNA methods).

#### 2.12. Effect of soil density and moisture on gamma response intensity

Soil density and moisture are parameters which could impact soil carbon measurement results when using PFTNA. While increasing soil density should increase the macroscopic


\* The measurement was made for one site on these plots.

Table 4. Average carbon weight percent in the upper soil layer by dry combustion and PFTNA methods [79].

Location Site # or Plot # MINS measurements Dry combustion measurements

Camp Hill Open Field OF1 2.20 0.29 2.230.45 2.85 0.25 2.250.51

Camp Hill Applied Field 2 AF2-1 1.22 0.38 1.590.45 2.00 0.34 1.480.46

Camp Hill Applied Field 3 AF3-1 1.44 0.43 1.770.37 1.96 0.34 1.900.53

Camp Hill Applied Field 4 AF4-1 2.59 0.42 2.330.34 1.58 0.34 2.120.46

E.V.Smith\* Plots S220 - - 0.930.61 - - 0.930.18

Table 4. Average carbon weight percent in the upper soil layer by dry combustion and PFTNA methods [79].

\*

166 New Insights on Gamma Rays

The measurement was made for one site on these plots.

AF2-2 2.09 0.37 1.14 0.34 AF2-3 1.46 0.37 1.31 0.08

AF3-2 1.68 0.37 1.34 0.34 AF3-3 2.17 0.39 2.4 0. 8

AF4-2 2.47 0.37 2.35 0.34 AF4-3 1.94 0.45 2.42 0.14

S320 - - 0.920.61 - - 1.400.05 S104 - - 0.340.68 - - 1.060.09 S114 - - 0.810.62 - - 1.410.53 S102 - - 0.930.62 - - 1.040.11 S112 - - 1.490.68 - - 1.510.55

w%

OF2 2.51 0.29 2.54 0.31 OF3 1.76 0.22 1.91 0.13 OF4 1.88 0.23 2.99 0.94 OF5 2.82 0.25 3.03 0.37 OF6 2.15 0.21 1.99 0.26 OF7 2.77 0.32 1.92 0.41 OF8 2.52 0.25 2.44 0.15 OF9 2.06 0.26 1.79 0.27 OF10 2.17 0.27 2.25 0.45 OF11 2.39 0.22 2.23 0.30 OF12 3.11 0.31 2.91 0.47 OF13 1.44 0.25 1.49 0.42 OF14 1.93 0.29 1.80 0.19 OF15 1.86 0.27 1.67 0.25

Plot average STD, w%

Carbon, w%

STD, w%

Plot average STD, w%

Carbon, w% STD,

Figure 29. Average values of carbon weight percent for 10-cm soil layer measured by dry combustion (diamonds) and PFTNA (circles) methods for the open field (OF) site at Camp Hill (points) and average field values (solid lines) [79].

Figure 30. Carbon content maps of the upper layer of the open field (Piedmont Research Unit, Camp Hill, AL): (a) neutron-gamma analysis (PFTNA method) and (b) chemical analysis (dry combustion).

cross section of neutron interactions with soil nuclei, the excited soil volume could decrease. Thus, at first glance the effect of soil density on the peak of interest areas in the soil net INS spectra is not significant. The presence of soil moisture increases the amount of hydrogen atoms that run to faster neutron moderation and can decrease the peak of interest areas in the soil net INS spectra due to a decrease in fast neutron numbers. Using the Geant4 tool kit [82], MC simulations of gamma spectra for carbon-sand mixtures with different densities and moistures were conducted to estimate their effect on gamma response intensity. Both the INS and TNC spectra were simulated; the TNC processes were inactivated at INS spectra simulation (commands: /process/activate neutronInelastic, /process/inactivate nCapture, neutron data library JENDL4.0), while the INS processes were inactivated at TNC spectra simulation (commands: / process/activate nCapture, /process/inactivate neutronInelastic, neutron data library G4NDL4.5).

The simulated INS and TNC spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbonsand mixtures of different densities (from 1.1 to 1.52 g/cm3 ) are shown in Figure 31. The dependencies of peak of interest areas with centroids at 1.78 and 4.45 MeV in the INS spectra with densities are shown in Figure 32. As can be seen in these figures, there are no significant changes in the spectra or peak areas. Thus, there is no significant effect of soil density on the INS spectra.

Simulated INS and TNC spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbon-sand mixtures having different moistures H (H from 0 to 30%; a real range of soil moisture change) are shown in Figure 33. The dependencies of peak of interest areas with centroids at 1.78 and 4.45 MeV in the INS spectra with water weight percent W [W=H / (1+H)] are shown in Figure 34. As seen in these figures, the peak areas slightly decrease throughout

Figure 31. Geant4 simulated INS (JENDL4.0, 1e9 events) and TNC (G4NDL4.5, 1e9 events) gamma spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbon-sand mixtures with different densities (from 1.1 to 1.52 g/cm3 ) irradiated by 14.1 MeV neutrons.

cross section of neutron interactions with soil nuclei, the excited soil volume could decrease. Thus, at first glance the effect of soil density on the peak of interest areas in the soil net INS spectra is not significant. The presence of soil moisture increases the amount of hydrogen atoms that run to faster neutron moderation and can decrease the peak of interest areas in the soil net INS spectra due to a decrease in fast neutron numbers. Using the Geant4 tool kit [82], MC simulations of gamma spectra for carbon-sand mixtures with different densities and moistures were conducted to estimate their effect on gamma response intensity. Both the INS and TNC spectra were simulated; the TNC processes were inactivated at INS spectra simulation (commands: /process/activate neutronInelastic, /process/inactivate nCapture, neutron data library JENDL4.0), while the INS processes were inactivated at TNC spectra simulation (commands: / process/activate nCapture, /process/inactivate neutronInelastic, neutron data library G4NDL4.5). The simulated INS and TNC spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbon-

dependencies of peak of interest areas with centroids at 1.78 and 4.45 MeV in the INS spectra with densities are shown in Figure 32. As can be seen in these figures, there are no significant changes in the spectra or peak areas. Thus, there is no significant effect of soil density on the INS spectra.

Simulated INS and TNC spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbon-sand mixtures having different moistures H (H from 0 to 30%; a real range of soil moisture change) are shown in Figure 33. The dependencies of peak of interest areas with centroids at 1.78 and 4.45 MeV in the INS spectra with water weight percent W [W=H / (1+H)] are shown in Figure 34. As seen in these figures, the peak areas slightly decrease throughout

Figure 31. Geant4 simulated INS (JENDL4.0, 1e9 events) and TNC (G4NDL4.5, 1e9 events) gamma spectra for 150 cm · 150 cm

· 60 cm pits with 5w% carbon-sand mixtures with different densities (from 1.1 to 1.52 g/cm3

) are shown in Figure 31. The

) irradiated by 14.1 MeV neutrons.

sand mixtures of different densities (from 1.1 to 1.52 g/cm3

168 New Insights on Gamma Rays

Figure 32. Dependencies of peak areas with centroids at 1.78 and 4.45MeV with different densities in INS spectra shown in Figure 31.

Figure 33. Geant4 simulated INS (JENDL4.0, 1e9 events) and TNC (G4NDL4.5, 1e9 events) gamma spectra for 150 cm · 150 cm · 60 cm pits with 5w% carbon-sand mixtures with different moistures (from 0 to 30%) irradiated by 14.1 MeV neutrons.

Figure 34. Dependencies of peak areas with centroids at 1.78 and 4.45 MeV with water weight percent in the INS spectra shown in Figure 33.

Figure 35. Dependencies of peak areas with centroids at 2.22, 3.53, and 4.93 MeV with water weight percent in the TNC spectra shown in Figure 33.

the possible moisture range. However, these changes are not significant in the practical range of soil moisture. Thus, it was concluded that moisture has no significant effect on the INS spectra.

At the same time, TNC spectra of gamma response increase with increasing soil moisture. In the studied moisture range, some peaks increase in direct proportion to the water weight percent in soil (e.g., TNC Si peaks with centroids at 3.53 and 4.93 MeV). The hydrogen peak with a centroid at 2.22 MeV increases as a square of the water weight percent within error limits (Figure 35). This probably occurs due to the linear increase of both thermal neutron flux and number of hydrogen nuclei as water weight percent increases. The conclusions regarding moisture and density effects on soil INS gamma response spectra agree with findings of others [15, 87].

#### 3. Conclusion

Figure 34. Dependencies of peak areas with centroids at 1.78 and 4.45 MeV with water weight percent in the INS spectra

Figure 35. Dependencies of peak areas with centroids at 2.22, 3.53, and 4.93 MeV with water weight percent in the TNC

shown in Figure 33.

170 New Insights on Gamma Rays

spectra shown in Figure 33.

Results of the PFTNA and the "gold standard" chemical analysis (Dry Combustion Technique) demonstrated good agreement for soil carbon content measurements in the upper soil layer (~10 cm). Experimental results successfully demonstrated that the average carbon weight percent in the upper soil layer (regardless of carbon depth distribution shape) can be measured in situ by the PFTNA measurement method (1 h) with accuracy comparable to the "gold standard" technique. The described procedures for background accountability, system calibration, and "soil carbon net peak" area calculations from the acquired spectra should be utilized. Although the current mobile system for PFTNA is fully capable of routine soil carbon measurements in natural and agricultural field settings, future modifications of the detector system and shielding can improve measurement accuracy and decrease measurement time. Nevertheless, the main features and herein described procedures (i.e., system background determination, calibration procedure, and "soil carbon net peak" area extraction) indicate that PFTNA methods can be recommended as a viable alternative procedure for soil carbon measurement. Additionally, MC simulations showed that soil density and moisture do not significantly impact soil carbon measurements by PFTNA.

#### Acknowledgements

The authors are indebted to Barry G. Dorman, Robert A. Icenogle, Juan Rodriguez, Morris G. Welch, and Marlin Siegford for technical assistance in experimental measurements, and to Jim Clark and Dexter LaGrand for assistance with computer simulations. We thank XIA LLC for allowing the use of their electronics and detectors in this project. This work was supported by NIFA ALA Research Contract No ALA061-4-15014 "Precision geospatial mapping of soil carbon content for agricultural productivity and lifecycle management."

#### Author details

Aleksandr Kavetskiy, Galina Yakubova, Stephen A. Prior and Henry Allen Torbert\*

\*Address all correspondence to: allen.torbert@ars.usda.gov

USDA-ARS National Soil Dynamics Laboratory, Auburn, Alabama, USA

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Author details

172 New Insights on Gamma Rays

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