New Agrophysics Divisions: Soil Quality Assessment

http://www.agrophysical.eu/ojs/index.php/eajournal/index

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New Agrophysics Divisions: Soil Quality Assessment and Zoning of an Agricultural Field Using a Fuzzy Indicator Modeling, Physic-Technical Bases of Plant Breeding, and Materials Based on Humic Acids (review) Dmitry Kurtener1 , Cai Dianxiong2, Victor Dragavtsev3, Elena Krueger1, Yu Zheng4, H.A. Torbert5 , Sergey Tsukanov6, Uwe Werner1 1

European Agrophysical Institute, Illighausen, Switzerland Chinese Academy of Agriculture Sciences, Beijing, China 3 Agrophysical Research Institute, St. Petersburg, Russia 4 Qingdao University, Qingdao, Shandong prov., China 5 USDA-ARS National Soil Dynamics Laboratory, Auburn, AL, USA 6 German-Ukrainian Centre for Innovative Industrial Agritechnologies, Kharkov, Ukraine 2

Correspondence: Dmitry Kurtener, European Agrophysical Institute, Kirchstr.10, 8574 Illighausen, Switzerland, email: [email protected] Received: July, 20, 2017; Accepted: September 14, 2017; Online: September 30, 2017 http://dx.doi.org/10.17830/j.eaj.2017.04.070

Abstract This work is devoted to review the new scientific divisions that emerged in agrophysics in the last 10-15 years. Among them are the following: 1) soil quality assessment using a fuzzy indicator modeling, 2) zoning of an agricultural field using a fuzzy indicator modeling, 3) agrophysical and physic-technical bases of plant breeding, and 4) development, testing, and application of materials based on humic acids for improving soil fertility and solving environmental problems. In recent years, the development of these scientific divisions has been associated with the activities of the European Agrophysical Institute and, in particular, with the creation (in 2013) of the European Agrophysical Journal (EAJ). On the initiative of the European Agrophysical Institute, a branch of the Institute in China was created. The planned work of this branch include research on the stabilization of soil degradation processes, improving soil fertility, and solving environmental problems. Keywords: agrophysics, soil quality assessment, zoning of an agricultural field, materials based on humic acids How to cite this paper: Kurtener, Dmitry, Dianxiong, Cai, Dragavtsev, Victor, Krueger, Elena, Zheng, Yu, Torbert, H.A., Tsukanov, Sergey, & Werner, Uwe. New agrophysics divisions: application of ANFIS, fuzzy indicator modeling, physictechnical bases of plant breeding, and materials based on humic acids (review). European Agrophysical Journal, 4(3), 61 - 80. http://dx.doi.org/10.17830/j.eaj.2017.04.070

This work is licensed under a Creative Commons Attribution 3.0 License.

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1. Introduction This work is devoted to review the new scientific divisions that emerged in agrophysics in the last 10-15 years. Among them are the following: 1. Soil quality assessment using a fuzzy indicator modeling, 2. Zoning of an agricultural field using a fuzzy indicator modeling, 3. Agrophysical and physic-technical bases of plant breeding, 4. Development, testing, and application of materials based on humic acids for improving soil fertility and solving environmental problems. The first review of new scientific divisions indicated under number 1, and 2 was given by Kurtener & Yakushev (2014). In recent years, the development of these agrophysics divisions has been associated with the activities of the European Agrophysical Institute and, in particular, with the creation of the European Agrophysical Journal (EAJ). It should be noted that Google-based Impact Factor of EAJ for 2015 was equal 2.23 and for 2016 - 2.00. Many articles written in the framework of these divisions have been published in EAJ. In this paper, reviews of these new agrophysics divisions are provided. On the initiative of the European Agrophysical Institute, a branch of the Institute was created in China. The planned work of branch includes the following scientific research: a) study of soil degradation processes, and b) development, testing, and application of materials based on humic acids for the stabilization of soil degradation processes, improving soil fertility, and solving environmental problems. Fragment of Agreement is given below.

Agreement on joint creation

共建欧洲农业物理（新乡）研究院

Branch of the EAI in Xingxian City

合作协议甲方：国家863（新乡）科技产业园

Party A: Program 863 - State Program High-tech Development Plan, Xingxian City

乙方：欧洲农业物理研究院（EUROPEAN AGROPHYSICAL

Party B: European Agrophysical Institute (hereinafter referred to as EAI)

INSTITUTE）

With the purpose of introduction of advanced 为促进欧洲农业物理研究院先进农业科研成果 agricultural technologies of the EAI in the agrarian 与新乡农业生产相结合，因地制宜、协同创新 industry of Xingxian City, as well as the use of local conditions and resources and the creation of ，实现经济效益和社会效益双丰收。甲乙双方 62


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an innovative system for achieving economic and 就共建“欧洲农业物理（新乡）研究院”相关事宜 social interests in the region, on the principles of mutually beneficial and mutually conditions terms, ，本着共建共享、互利互赢的原则，经友好协 the parties agreed to establish an EAI branch in 商特制定本合作协议 Xingxian City…

2. Soil quality assessment using fuzzy modeling written by Dmitry Kurtener, Allen Torbert & Elena Krueger

In recent years, soil quality has been the subject of many investigations and there is a large and growing scientific literature base covering this subject. However, results of these extensive studies do not include evaluations of soil quality as a “degree or grade of perfection”. An effective methodology which allows for the evaluations of soil quality as a “degree or grade of perfection” is the fuzzy multi attributive approach. This approach may help soil scientists to solve complex soil quality problems in a systematic, consistent, and more productive way. According to the Glossary of Soil Quality, the term “indicator of soil quality” is defined as follows: “A quantitative or qualitative measure used to estimate soil functional capacity. Indicators should be adequately sensitive to change, accurately reflect the processes or biophysical mechanisms relevant to the function of interest, and be cost effective and relatively easy and practical to measure. Soil quality indicators are often categorized into biological, chemical, and physical indicators.” It is easy to see that the accepted variants of the term “soil quality indicator” do not define indicator as “degree or grade of perfection”. The first article on soil quality assessment using fuzzy indicators was written by Torbert et al. (2008). In this work, the fuzzy indicator concept (FIC) was considered as a general platform to assess soil quality as a “degree or grade of perfection”. Two general types of FSQI can be defined: individual fuzzy soil quality indicators (IFSQI) and combined fuzzy soil quality indicators (CFSQI). To demonstrate the theoretical consideration for the application of CFSQI to soil quality, a simple example was illustrated by et al. (2008). In this example, fuzzy indicator modelling was applied for the evaluation of soil quality of an agricultural field located in Bell County, Texas, USA. The site was divided into two fields with one field managed based on conventional farming practices, and the other field managed with precision farming concepts. The soils within the study site consisted of a Heiden clay (fine, montmorillonitic, thermic Udic Chromusterts), a Houston black clay (fine, montmorillonitic, thermic Udic Pellusterts), and a Ferris clay (fine, montmorillonitic, thermic Udorthertic Chromusterts). Soil samples were collected from a 6 inch depth at the initiation of the study on a one ha grid and analyzed for total N, total P, and organic C concentrations (Torbert et al., 2000).

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For definition of parameters of individual fuzzy indicators in this example, an empirical model formulated by Kaiumov (1977) was used. Kaiumov analyzed suitability of yield-controlled factors for crops and defined the intervals of soil attributes, which are more suitable for crops (Table 2.1). In other words, according to Kaiumov’s empirical model, there exists an interval for a soil attribute so that if the values of this attribute lies within this interval then it is the best conditions for soil quality. It should be noted that this model has considerable shortages. However, it is no matter, because aim of this example is to illustrate the suggested approach.

Table 2.1. Intervals within which values of soil attributes are more suitable for crops (Kaiumov, 1977) pH

SOM %

P2O5 mg kg-1

K2O mg kg-1

Loam

6.5 - 7

1.8 - 2.2

250 - 280

200 - 260

Loamy sand

6 – 6.5

2 – 2.4

200 - 250

180 - 200

Sandy

5.5 - 6

2.2 – 2.6

180 – 200

140 - 160

Turf

5 – 5.5

-

500 - 600

600 - 800

Soil

Figure 2.1 illustrates this with an example of an individual fuzzy indicator for K2O.

Fig. 2.1. The individual fuzzy indicator for K2O.

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Figure 2.2 illustrates result of soil quality evaluation using composite fuzzy indicator (CFSQI) based on the combination of two IFSQIs on available phosphorus and soil organic concentration.

Fig. 2.2. Composite fuzzy indicator based on the combination of two IFSQIs on available phosphorus and soil organic concentration.

3. Zoning of an agricultural field using a fuzzy indicator modeling written by Dmitry Kurtener, Allen Torbert & Elena Krueger

Within-field variability is a well-known phenomenon in agriculture and is central to the precision farming concept. One way of dealing with this problem is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). However, decisions must be made as to how these management zones will be delineated. The evaluation of MZs delineation is the subject of many scientific research studies. The delineation of management zones could be based on factors such as soil and field characteristics (Fridgen, 2000; Fridgen et al., 2004), digital elevation model (Pilesjo et al., 2000) and yield maps 65

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(Stafford et al., 1999). Another method is based on the use of GIS software. Yield values are calculated on a cell-by-cell basis and a map of average yield values can be created (Mitchell, 1999). The most developed approach is based on some sort of clustering methods. Clustering with the fuzzy k-means algorithm (fuzzy c-means) is described by Tou & Gonzalez (1974), Fraisse et al. (1999), and Yakushev & Bure (2007) discussed a method for recognizing relatively homogeneous zones based on limit theorems of probability theory. In recent years, some progress in the study of within-field variability has been achieved by application of a fuzzy indicator model (Kurtener et al., 2008, 2011, 2013; Torbert et al., 2009, 2014). Using this model, it is possible to achieve agricultural field zoning on the bases of the combination of several soil and crop characteristics. This paper reports on the development of a fuzzy indicator model for definition of zones with different levels of productivity within an agricultural field. The theoretical considerations are illustrated with this example based on data collected from a precision agriculture study in central Texas, USA (Torbert et al., 2000). Preliminary analysis of the experimental data showed that the experimental field was relatively homogeneous. Nevertheless, it can be subdivided by two zones with the medium productivity (Z 1) and good productivity (Z 2) (Table 3.1).

Table 3.1. The ranges of parameters The ranges of parameters within experimental field The ranges of parameters within zones with the medium productivity

Total C, %

Total N, %

Available P, %

Yield, Bu/acre

1.95 - 3.04

0.089 - 0.134

0.027 - 0.043

102 - 135.51

1.5 – 2.4

0.05 - 0.1

0,015 - 0,03

100 - 120

2.41 – 3.5

0.11 – 0.27

0,031 - 0.045

121 - 140

(Z 1) The ranges of parameters within zones with the good productivity (Z 2)

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Results of calculations of fuzzy indicators are shown in figure 3.1.

Fig. 3.1. Area within agricultural field with a medium level of productivity defined on the basis of values of fuzzy indicator CFI.

4. Agrophysical and physic-technical bases of plant breeding written by Victor Dragavtsev, & Dmitry Kurtener

Today’s world experience shows that a technogenic way of intensification of crop production cannot solve the problem of further increasing yields. This is due to rising energy costs and a violation of the ecological balance in nature. The global crisis in agricultural production of the XXI century requires a new strategy - biologization of crop, i.e., the creation of new types of plant varieties, which are resistant to abiotic and biotic factors of the environment (Report of FAO, 2014).

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The most important lever for increasing plant productivity today is genetic-breeding technologies, which are significantly cheaper than the use of traditional agricultural technologies. The use of a new variety to increase productivity will be "working" in the field for 7-10 years, without requiring a large capital investment, while traditional agricultural technologies must be applied annually, and with some of them - several times during the year (Dragavtsev & Kurtener, 2016). In 1935, Vavilov wrote: "in the past the care for the soil - fertilizer, tillage, etc, came to the fore. But our main goal is in the other - in agricultural plant building". International experience shows that the express methods, which provide an opportunity to study an effect of “genotype-environment” interaction, are usually created with methods of biophysics and for plant breeding - on the basis of agrophysics. Today it has become obvious that agrophysical approaches and methods will find their place not only in the improvement of agricultural technologies, but they could also revolutionize the breeding technologies”. Tasks for interdisciplinary research related to the application of agrophysics for epigenetics was formulated by Dragavtsev et al. (2011) and Dragavtsev & Kurtener (2016). At first, it is necessary to develop methods to quickly identify the best genotypes by their phenotypes into natural populations and isolated breeding segregated populations. Research by Lobashёv (1966) indicated that the success of the application of genetic knowledge to the breeding process is determined by the development of rapid diagnostic methods for increasing speed of estimation of organism genotypes based on characters of productivity. The theory of the rapid identification of genotypes by their phenotypes was created by Dragavtsev & Pesek (1977) and was named "Background Traits Principle" (BTP). Kocherina (2007) has shown that on the basis of the BTP it is possible to improve the exactness of recognition (identification) of individual genotypes in a segregated population by 1000 times. This is especially important for the breeding for drought tolerance. At present, application of BTP in Russia indicates that the reliability of the "recognition" (identification) genotypic values of each individual plant in the wild or in the segregated breeding population can be increased by hundreds of times. In order to implement this principle in the breeding field, it is necessary to develop portable devices for rapid measurement of different background signs (impedance refractometric index, water content of tissues, etc.) in the field. According to Dragavtsev & Kurtener (2016), the second research task can be described as follows: development of tools for separation of the hybrid heterotic seeds from the non-hybrid seeds. Recently, plant breeding for heterosis has expanded rapidly to cover more and more new plant species. Therefore, one of the most important tasks is the separation of heterotic (heterozygote) seeds from non-heterotic (homozygotes). Vlasov et al. (1977) suggested the use of paramagnetic resonance method for the separation of heterotic seeds from non-heterotic seeds into the F1 generations of hybrids. The breeding company "Petkus" (Germany) created the original automatic separator for purification of hybrid heterotic seeds from the non-heterotic seeds of winter rye. The third task for interdisciplinary research was determined as follows: laser bean identification of oligogene using the absorption spectra of the laser. Recently, research has focused on using laser beam technology for the identification of oligogene by using the absorption spectra of the laser beams within 68


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certain frequencies that are produced from the products of these genes. A patent for the invention (Koshkin et al., 2005) was elaborated by the Agrophysical Research Institute in collaboration with All Russian Institute of Plant Genetic Resources (Dragavtsev & Kurtener, 2016). Finally, the fourth task for interdisciplinary research was described as follows: plant breeding in phytotron growth chambers. The use of phytotron growth chambers for plant breeding is considered by Dragavtsev (2008) from the standpoint of modern organization of plant breeding processes and the use of agrophysical methods. A phytotron growth chamber can be used to impose the typical dynamics of environmental limiting factors for any point on the globe, including imposing these conditions on the ontogenesis phases of plant growth. There are many very important areas of research that could potentially benefit from plant breeding, but the conditions needed to be studied cannot be realized under field conditions. For example, the study of the resistance of plants to acidic or saline soils, or the research of the resistance of plants to the negative impacts of the UV ozone holes in the atmosphere are impossible to impose in the field (Dragavtsev & Kurtener, 2016, 2017).

5. Development, testing and application of materials based on humic acids for improving soil fertility and solving environmental problems. written by Uwe Werner, Cai Dianxiong, Dmitry Kurtener, Sergey Tsukanov, & Yu Zheng

5.1. Organic-mineral fertilizers (OMF), based on sapropel and peat Organic-mineral fertilizers (OMF) based on sapropel and peat have recently generated considerable interest. These include the products called “Humin Plus”, “Sapropeet”, “Sapropeet Uni”, “Sapro Agro”, and “Sapro Elixir”. Sapro Agro (manufactured by LLC LATPOWER, Riga, Latvia) is a biologically active soil conditioner, produced on environmentally sound technology from natural ingredients: sapropel colloid and active peat (http://www.latpower.lv/index.php?page=2). Sapro Elixir (manufactured by SIA HUMIN VIT, Ogre, Latvia) is produced from natural fresh water lake sapropel with natural moisture. Sapro Elixir contains a full spectrum of natural biologically active ingredients. The product is balanced with limited amounts of NPK and nutrients (http://www.huminvit.lv/sapro-elixir?lang=eng. Sapro Elixir is a highlyefficient natural-organic fertilizer and soil enhancer that can be applied on all soil types and all kinds of fruit and vegetables, sowing and decorative crops, trees, and bushes. Humin Plus/Sapropeet is an organic mineral micro-fertilizer based on sapropel extract (http://athdevelopments.co.uk/downloads/Humin%20Plus%20Brochure.pdf). This product has a German patent application 10 2012 100 315.7 dd. 15.01.2012 «Reagent-free mode of getting reception humus containing suspensions of the raised biological efficacy from connatural raw materials (sapropel, peat and organic waste - chicken manure), and production on the basis of its organic and organic mineral fertilizers for ecological and traditional farming». A licensed technology based on new physical principles in the processing of raw materials (cavitation combined with magnetic treatment) is used to obtain micro fertilizers with new characteristics (i.e.: improved consistency and increased physiological 69

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and biological activity of the ingredients http://athdevelopments.co.uk/downloads/Humin%20Plus%20Brochure.pdf). Sapropeat Uni (http://www.sapropeatafrica.com/sapropeat.html) is a biologically active product aimed at rectifying soil health.

5.2. Testing organic-mineral fertilizers based on sapropel and peat in countries of Europe Testing organic-mineral fertilizers Humin Plus/Sapropeet was carried out in several countries of Europe. The main results of testing of Humin Plus are given in Tables 5.2.1 throw Table 5.2.4.

Table 5.2.1. Experimental data characterized increase yields in Germany Cooperating organization DANISCO Sugar ltd. Anklam sugar factory Biowork ltd. Agrar ltd. Vipperow ARGE ltd.

Date

Agricultural plants

Increase yield,%

2010 2011 2012 2011 2008 2009

Sugar beet Sugar beet Sugar beet Corn Organic potato Sugar beet

11 12 12 18 28 15

Table 5.2.2. Experimental data characterized increase yields in Russia Cooperating organization AgRosHleboproduktltd.

Date

Agricultural plants

Increase yield,%

2006

Corn

24

Table 5.2.3. Experimental data characterized increase yields in Belarus Cooperating organization The administration of Valozhynskogo district, Minsk region. Republican Unitary Enterprise "Experimental Station on sugar beet" of the National Academy of Sciences of Belarus 70

Date

Agricultural plants

Increase yield,%

2006

Sugar beet

26

2007

Sugar beet

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Table 5.2.4. Experimental data characterized increase yields in Ukraine Date

Agricultural plants

Increase yield,%

Karazina Kharkiv National University

2010 2011 2012

Winter wheat Tomato Potato

23 24 32

Krymagroproekt Institute ltd.

2008

Corn

25

Cooperating organization

These experiments show, that increased yields were equal to 12-32% for sugar beet, 18-27% for corn, 30-36% for potato, 24-28% for organic potato, 23-25% for winter wheat, 18-22% for spring wheat, 18 25% for tomato, 15-20% for barley, 45-55% for canola, and 30- 36% for soybean.

5.3. Testing organic-mineral fertilizers based on sapropel and peat in countries of the Middle East Several experiments on the effects of the application of organic-mineral fertilizers (OMF) based on sapropel and peat were carried out in Middle East countries (Ostrovskij, 2014; Ostrovskij et al., 2014; Tsukanov et al., 2014). Results of experiments using Sapro Agro and Sapro Elixir show that: a) yield of sweet pepper, cucumber, and tomato increased by 18-20%, water for irrigation was reduced by no less than 15-20%, and there was a significant reduction of fertilizer utilization; b) utilization of these OMF provided an accelerated growth of seedlings; and c) yield of fruits such as peaches, pears, grapes, lemons, and oranges were increased, and water for irrigation was reduced. Results of experiments using Humin Plus/Sapropeet indicated that: a) yield of cucumbers in greenhouses, where there was a pre-treatment stage of soil, increased by 24%; b) yield of cucumbers treated by Humin Plus/Sapropeet were increased by 31%; c) OMF application was very effective in the early stages of fruiting when the plant needed quick delivery of nutrients; d) correct application rates resulted in higher yields and extended fruiting; and e) it is necessary to repeat the OMF treatment every 10-14 days for optimal results. Most of the farmers who conducted experiments indicated a desire to acquire the Humin Plus/Sapropeet products for future use (Tsukanov et al., 2014).

5.4. Influence of the humic acids on the absorption of heavy metals in soil The study of the interaction of humic acids with heavy metals in soil has been the subject of many research manuscripts (Zavarzina, 2000; Van den Hoop et al., 2002; Lishtvan et al., 2006; Tang et al., 2014). For example, Van den Hoop et al. (2002) researched the complex interactions of heavy metals with humic acids. Zavarzina (2000) wrote that within soil, humic acids continuously react with metal ions in the surrounding soil solution and the solid phase of soil minerals. These interactions are one of the most important factors responsible for the formation of soil structure and soil profiles (Zavarzina, 71


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2000). Of particular interest is the study of the mechanism involved with the processes associated with the interaction of humic acids with heavy metal ions, as well as the peculiarities of the structure and properties of the metal-humic complexes that are formed (Lishtvan et al., 2006). There are published reviews of this subject, including Tang et al. (2014) who published on the theme: "The impact of humic and fulvic acids in the process of removing heavy metals from aqueous solutions using nanomaterials”. The study of the influence of the Humin Plus series (in particular Geoplus) on the absorption of heavy metals by soil was conducted in 2006 by the V. I. Vernadsky Taurida National University, and was ordered by the German-Russian Institute of Cybernetics, Biomagnetic and Nanotechnology. The results of the study show that after application of a 2% dose of Geoplus, the soluble forms of cadmium are reduced by a range of 0.9 to 1.2% and the soluble forms of lead were reduced by a range of 1.4 to 10%. Thus the results indicate that the application of the Humin Plus series (in particular Geoplus) to soil can be a highly effective means to decrease the soluble forms of heavy metals in soil.

5.5. Extension of this research with establishment of a China Branch of the European Agrophysical Institute (CBEAI) During the period from July 10 to July 19, 2017, the representatives of the European Agrophysical Institute negotiated with the provincial, city and district administrations of Xinxiang City, China (the party leadership of the city and the region) the authority of Technopark 863 (figures 5.5.1 and 5.5.2). The result was the decision to establish a China Branch of the European Agrophysical Institute (CBEAI) on the basis of the Technopark 863. The objectives of research to be carried out are: 1. Top soil of a specific region of China (detection of agrochemical and microbiology characteristics) 2. Organic raw materials: sapropel, peat, poultry waste, and other organic materials 3. Organic and organic mineral fertilizers, soil improvers, stimulators/regulators of plant growth, and biological products. The planned scientific research includes the following:  Conducting physicochemical, agrochemical, and microbiological analyzes of research objectives.  The study of degradation processes in soils.  Interpretation of results.  Development of technologies for stabilization of degradation processes and improvement of soil fertility.  Development of organic and organic-mineral fertilizers, soil enhancers, and stimulators/regulators of plant growth that are promising for China.  For specific regions of China several recommendations and a number of scientific articles will be prepared.

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Fig. 5.5.1. Participants of negotiations (from left to right): 1) Yu Zheng, Dr., Prof., Qingdao University, Qingdao, Shandong prov., China, and an General representative of European Agrophysical Institute in China; 2) Sergey Tsukanov, Dr., Vice-president of European Agrophysical Institute, Illighausen, Switzerland; 3) Dengxi Wang, Mayor of Xinxiang Municipal People's Government, P.R. of China; 4) Wei Zhi, Vice-mayor of Xinxiang Municipal People's Government, P.R. of China; 5) Meilin Liu, Director, of Bureau of Science and Technology of Xinxiang, P.R. of China.

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Fig. 5.5.2. Participants of negotiations (final photo, from left to right): 1) Yidong Xiao, Executive director of European Agrophysical (Xinxiang) Institute, P.R. of China; 2) Yu Zheng, Dr., Prof., Qingdao University, Qingdao, Shandong prov., China, and General representative of European Agrophysical Institute in China; 3) Nikolay Artomov, Dr., Prof., Academician of Engineering Academy of Ukraine; 4) Sergey Tsukanov, Dr., Vicepresident of European Agrophysical Institute, Illighausen, Switzerland; 5) Dengxi Wang, Mayor of Xinxiang Municipal People's Government, P.R. of China; 6) Wei Zhi, Vice-mayor of Xinxiang Municipal People's Government, P.R. of China; 7) Meilin Liu, Director, of Bureau of Science and Technology of Xinxiang, P.R. of China; 8) Chunfeng Yu, General director of Xinxiang Technology Industrial Park, P.R. of China.

In connection with the completion of the negotiations, the presentation of the European Agrophysical Institute (figures 5.5.3 through 5.5.6) was organized in the CBEAI exhibition hall.

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Fig. 5.5.3. Opening of the presentation of the European Agrophysical Institute.

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Fig. 5.5.4. Exhibition materials: organic-mineral fertilizers (OMF) developed by the European Agrophysical Institute.

Fig. 5.5.5. Exhibition materials: issues of European Agrophysical Journal (EAJ) and micro OMF developed by the European Agrophysical Institute.

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Fig. 5.5.6. Exhibition materials: presentation of the leading scientists of the European Agrophysical Institute (top down in the left column: 1) Volodymyr Ladyka Academician of National Academy of Agrarian Sciences of Ukraine; 2) V.A. Dragavtsev, D.Sc., Prof., Academician of Russian Academy of Science; 3) Dmitry Kurtener, D.Sc., Prof.; top down in the right column: 1) H. Allen Torbert, PhD, Research Leader of National Soil Dynamics Laboratory, USA; 2) Sviatоslav Baliuk, Academician of NAASU, Ukraine).

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References Dragavtsev, V.A., Pesek, J. (1977). Estimation of genotypic and environmental variation in plants. In Alexander Hollanded. (Ed.). Enciklopaedia “Basic Life Sciences”, Vol 8, (pp. 233-240). New York – London – Washington – Boston. Dragavtsev, V.A. (2008). The basis of the future high-tech breeding technologies for eco-genetic improvement of economically important plant properties (in Russian). Agrarian Russia, 4, 2-10. Dragavtsev, V.A., Makarova, G.A., Kochetov, A.A., Kocherina, N.V., Mirskaya, G.V., & Sinyavina, N.G. (2011). Some tasks of agrophysical provision of breeding technologies for genetic enhancement of productivity and crop yield (in Russian). Agrophysics, 1. Retrieved from http://109.120.137.118/Media/Default/Page/Agrophys_magazine/N1/Dragavcev.pdf Dragavtsev, Victor, & Kurtener, Dmitry (2016). Agrophysics and epigenetics: tasks for interdisciplinary researches. European Agrophysical Journal, 3(4), 119 - 125. http://dx.doi.org/10.17830/j.eaj.2016.03.120 Dragavtsev, Victor, & Kurtener, Dmitry (2017). New scientific directions in agrophysics: epigenetics and breeding plants. European Agrophysical Journal, 4(1), 1 - 9. http://dx.doi.org/10.17830/j.eaj.2017.04.001 Fridgen, J. J. (2000). Development and evaluation of unsupervised clustering software for sub-field delineation of agricultural fields. M.S. thesis. Univ. of Missouri, Columbia. Fridgen, J., Kitchen, N., Sudduth, K., Drummond, S., Wiebold, W., & Fraisse, C. (2004). Management Zone Analyst (MZA): software for subfield management zone delineation. Agronomy Journal, 96, 100108. Kaiumov, M.K. 1977. Handbook on yield programming (In Russian). Moscow, Russia. Kocherina, N.V. (2007). Identification of genotypes of individual plants by their phenotypes on quantitative traits in segregated populations in the early stages of breeding (in Russian). Agricultural biology, 1, 96-102. Kurtener, Dmitry, Torbert, H. Allen, & Krueger, Elena (2008). Evaluation of agricultural land suitability: application of fuzzy indicators. In: Lecture Notes in Computer Science 0302-9743, Volume 5072/2008, Berlin / Heidelberg: Springer. Kurtener, D.A.,Yakishev V.P., Torbert H. A., & Krueger, E.D. (2011). Zoning of an Agricultural Field using a Fuzzy Indicator Model. In 11th International Conference on Precision Agriculture (pp. 157-163), Prague. Kurtener, D. A.,Yakishev, V. P., Torbert, H. A., & Krueger, E. D. (2013). Definition of zones with different levels of productivity within an agricultural field using fuzzy modelling. Journal of Agricultural Science, 5(5), 176-181.

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