Startseite Molecular studies for drought tolerance in some Egyptian wheat genotypes under different irrigation systems
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Molecular studies for drought tolerance in some Egyptian wheat genotypes under different irrigation systems

  • Hani A. Mansour EMAIL logo , Salwa El Sayed Mohamed EMAIL logo und David A. Lightfoot EMAIL logo
Veröffentlicht/Copyright: 8. Juli 2020
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Aus der Zeitschrift Open Agriculture Band 5 Heft 1

Abstract

This research work was carried out to evaluate drought stress for the differentiation of bread wheat (Triticum aestivum L.) genotypes in their ability to tolerate drought. An experiment was carried out on six genotypes (‘Sides 1’, ‘Shindwell 1’, ‘Gemmiza 9’, ‘Sakha 93’, ‘Saheel 1’ and ‘Masr 2’). A randomized complete block design with three replications along two separate tests under the drip and the sprinkler irrigation systems was used in this experiment. One of the irrigation treatments applied the normal amount of irrigation water and the other applied end-season drought stress conditions through two successful agricultural seasons 2016/2017 and 2017/2018. The impact on biomass, grain yield and water productivity of the six genotypes was measured. Random amplified polymorphic DNA (RAPD) markers were used to evaluate genetic variation among the six genotypes. PCR–RAPD analysis showed that there were several differences in both the size and number of bands between the varieties. Based on these markers, genetic similarity coefficients were calculated and a dendrogram was constructed. The dendrogram analysis delineated three major clusters. The current study showed that RAPD markers are useful in the assessment of the genetic diversity among the wheat genotypes. The drip irrigation system gave the highest values of both the biomass and the grain yield for the six genotypes, while the sprinkler irrigation gave the lowest values. Comparing the six genotypes in terms of the biomass, grain yield and water productivity, it was concluded that, the highest water productivity (WP) genotype was Sides 1, except during water stress with the sprinkler irrigation system. It was followed by genotype Sakha 93, then genotype Shindwell 1, while genotype Gemmiza 9 gave the lowest in all cases, except without water stress under drip irrigation.

Keywords: wheat genotypes; morphology; DNA (RAPD); irrigation systems; water productivity

1 Introduction

Wheat (Triticum aestivum L.) is one of the most important cereal crops, which is widely cultivated in Egypt and the world, providing food calories and protein to the human population. Drought is one of the important environmental challenges, which growers must face around the world. Drought causes huge grain losses every year, especially in the developing countries, and current trends in the global climate change will most likely lead to further losses (Caetano-Anolles et al. 1991; Martin et al. 1991; Kobata et al. 1996; Lopezcastaneda and Richards 1994; Cao et al. 2000; Ribaut et al. 2002; Morgante and Salamini 2003; Li et al. 2015). The problem of worldwide water shortage and uneven distribution of rainfall makes the improvement of drought tolerance very important (Luo and Zhang 2001).

In sandy soils and under different irrigation systems, there are positive relationships between the lack of water stress, the vegetative growth, the economics of yield and water productivity of various Egyptian wheat varieties. These relationships are both linear or nonlinear in nature (Mansour et al. 2015a,b, 2016; Mansour 2015).

Detailed climatic data are required because daily temperature changes often have a significant effect on economic yield, which affects water use efficiency, with both high temperature and low temperature causing the dry material to be less productive. This is evident in the production stage, and it is likely that the future productivity of the economic crop and the productivity of the water unit will be highly volatile, if the climate remains without fluctuations for long periods (Semenov and Porter 1995; Isik and Devadoss 2006). In response to climatic variability, the productivity of different varieties of crops is different (Ibrahim et al. 2018; Tayel et al. 2018).

The very large differences between crop productivity and gaps are defined when determining the maximum productivity of farmers. If the irrigation of wheat, maize and rice crops is carried out, the differences do not reach 80% of productivity (Lobell et al. 2009). It is possible to improve crop productivity for farmers by using modern techniques and highly sophisticated irrigation systems, based on sensors to determine the best management of adding nutrients and water together. Farmers’ use of these advanced technologies varies by location of activity and the potential of farmers, which, of course, leads to variation in crop productivity from one location to another and from one farm to another (Grassini et al. 2015).

Breeding for drought tolerance is a major objective in arid and semiarid regions of the world due to inadequate precipitation, shortage of irrigation water and the high-water demand for crop evapotranspiration in such climates. The lack of water in Egypt, due to the Grand Ethiopian Renaissance Dam, makes improving drought tolerance particularly important. The random amplified polymorphic DNA (RAPD) technique, based on the polymerase chain reaction (PCR), has been one of the most used molecular techniques to study DNA marker polymorphisms, which can occur due to the base substitutions at primer binding sites (Williams et al. 1993). Drought resistance is now considered by breeders and molecular biologists to be a valid breeding target. Also, in the past, the breeding efforts for improving drought tolerance were hindered by quantitative genetic (Passioura 2002). Tuberosa and Salvi (2007) stated that the genomics based on these approaches provide access to agronomically desirable alleles, which presented at the quantitative trait loci (QTLs), impact such responses, thus enabling us to more effectively improve the drought resistance and yield of crops under water shortage conditions. They commented on how to get the information on QTLs which govern the response to drought and the candidate genes responsible for QTL impacts can be used to elucidate the physiological basis of drought resistance and help select the genotypes with an improvement in yield under water shortage (Vierling and Nguyen 1992; Bardakci 2001).

The aim of the current study was to provide information on the molecular and genetic characteristics of six wheat genotypes for drought tolerance, and the effect of different irrigation systems and water stress on biomass, grain yield and water productivity of wheat.

2 Materials and methods

The methodology of the current study could be divided into two parts. The first part describes the irrigation system components and climate, soil conditions, wheat plants, cultivars, etc. of field experiments and the second part elucidates the laboratory study of the genomic engineering for evaluating the cultivars, which were most productive under drought conditions.

2.1 Field experiments

The current study was carried out in the Research and Production Station, National Research Centre, El-Nubaria City, El-Behira Governorate. During two successful seasons, two field trials were carried out in the 2016/2017 and 2017/2018 seasons, to find out the effect of both drip and sprinkler irrigation systems, water drought treatments (normal irrigation with 100% ET0 and stress with 50% ET0) and six wheat genotypes (Sides 1, Shindwell 1, Gemmiza 9, Sakha 93, Saheel 1 and Masr 2) on biomass, grain yield and water productivity.

2.2 Study location

The design of the experiment was in randomized complete blocks with three replicates. A soil sample was taken representatively during soil preparation from the surface layer (0–30 cm) for analysis before applying fertilizers. This analysis informed the application of N fertilizers as 50 kg urea/fed and 70 kg ammonium nitrate/fed; however, the normally recommended rates of P and K fertilizers were used. The physical soil properties were as follows: the soil was classified as sandy with high pH, low salinity and organic matter, with a medium level of calcium carbonate. Other measurements included: field capacity recorded (12 θw%), permanent wilting point (4.1 θw%), available water (7.9 θw%), bulk density (1.57 g/cm3) and hydraulic conductivity (6.76 m/h).

The main plots had drip and sprinkler irrigation systems, and the subplots were the two water drought treatments (normal irrigation with 100% ET0 and stress with 50% ET0). The details of the climatic data (ET0, Max and Min temperatures, GS, P and Ari) of the field location are shown in Table 1 and Figure 2. The six wheat genotypes were Sides 1, Shindwell 1, Gemmiza 9, Sakha 93, Saheel 1 and Masr 2. Biomass, grain yield and water productivity were evaluated.

Table 1

Climatic parameters of the year 2017 represented El-Nubaria climate station. Tm, mean temperature (°C); Tmax, highest temperature (°C); Tmin, lowest temperature (°C); P, precipitation (mm)

MonthsTmTmaxTminP, mmET0 (T)ET0 (H)
Jan.14.320.08.64.228.090.1
Feb.15.521.59.44.531.399.9
Mar.17.624.410.92.448.3113.2
Apr.21.328.713.91.377.3127.7
May24.632.216.90.4116.1132.3
Jun.27.435.019.90.0139.5134.9
Jul.29.236.421.90.1151.7139.2
Aug.29.236.122.30.0151.7143.7
Sep.27.734.321.00.0139.5142.8
Oct.24.030.217.81.0108.8126.8
Nov.19.725.613.81.863.0107.8
Dec.15.921.410.42.837.091.6
Annual22.228.815.618.51092.21450.1

The irrigation networks as shown in Figure 3. These components of the irrigation network were installed and operated, according to Mansour (2015), Mansour and Aljughaiman (2015), Mansourand El-Melhem (2015) and Mansour et al. (2015a,b).

Figure 2 Volumetric graph of the year 2017 represented climate station in the studied area. Tm, mean temperature in Celsius; P, precipitation (mm); ET0, potential evapotranspiration (mm); GS, Growing season; Ari, Aridity index.
Figure 2

Volumetric graph of the year 2017 represented climate station in the studied area. Tm, mean temperature in Celsius; P, precipitation (mm); ET0, potential evapotranspiration (mm); GS, Growing season; Ari, Aridity index.

Figure 3 Layout of irrigation systems, study factors and all treatments of the field experiments.
Figure 3

Layout of irrigation systems, study factors and all treatments of the field experiments.

The design of cultivated varieties was in randomized complete blocks, under both drip and sprinkler irrigation systems, where the plot size under the drip system was 12 m2 and under sprinkler system was 25 m2. Also, annual time of applied fertilizer was 25/12 for each season with 50 kg urea/fed, and 70 kg ammonium nitrate/fed, but P and K fertilizers were added according to the recommended scheduling by the Egyptian Ministry of Agriculture and Land Reclamation.

The drought susceptibility index (DSI) was calculated using the formula given by Fischer and Wood (1979):

DSI=1−Yd/Yp/D

where, Yd = seed yield of the genotypes under moisture stress condition. Yp = seed yield of the genotypes under irrigated condition. D = mean yield of all strains under moisture stress condition/mean yield of all strains under irrigated condition.

Drought tolerance efficiency (DTE) was estimated using the formula given by Fischer and Wood (1981).

DTE(%)=Yieldunderstress/yieldundernonstress×100

2.3 Plant material

The PCR analysis was conducted using genomic DNA from the six wheat varieties (Sides 1, Shindwell 1, Gemmiza 9, Sakha 93, Saheel 1 and Masr 2) and cereals of wheat varieties (Sides 1, Shindwell 1). These varieties were obtained from the Agricultural Research Station in Shindawil Island (Regional Research Station of Upper Egypt in Shandawil) and wheat varieties such as Gemmiza 9, Sakha 93, Saheel 1 and Masr 2 were obtained from the Field Crops Institute in Nubaria Agricultural Research Station. Water-sensitive wheat varieties (Masr 2 and Gemmiza 9) and -resistant varieties (Sakha 93 and Saheel 1) were used. The wheat varieties were grown in pots.

2.4 DNA extraction

Small leaves, of about 500 mg weight, were frozen and converted into powder by grinding them with liquid nitrogen. This powder was added to tubes of 9.0 mL of a CTAB temporary storage drive to extract. These mixtures were incubated at a temperature of 650°C for 60–90 min. After this, 4.5 mL of chloroform/octanol (1:24) was added, and the tubes were shaken for 10 min using a centrifuge (3,200 rpm). The floating material in the new tubes was transferred into 6 mL of isopropanol and left for an hour (Williams et al. 1990, Rohlf 1993, Sun et al. 2003, Tolk and Howell 2003, Roy et al. 2004, Salvi and Tuberosa 2005). Then they were placed in a centrifuge for 10 min, and the small balls obtained were placed in tubes called sterile Eppendorf, which contained 400 TE of buffer with 108.0 mL of hydrochloric acid, and a pH of 8.0, and 1.0 mL of a substance EDTA, pH = 8.0 (thus, the DNA was extracted from the genotypes from the wheat leaves and it was stored at minus 20 degrees Celsius for use by Doyle and Doyle (1990)).

2.5 Determination of DNA concentration by ultraviolet spectroscopy

The DNA was diluted by adding 20 µL of the DNA solution to 0.98 mL of distilled water in a small tube for centrifugation and good mixing, and heating by UV lamp for 20 min. The wavelength of the optical spectrum was set to 260 nm. Distilled water was added to the mixture, and the extent of the absorption of the whipping of the DNA was measured. So, the DNA concentration was calculated, according to Sambrook et al. (1989).

2.6 Amplification by PCR

Table 3 illustrates that the Pharmacia biotechnology test was carried out by Amersham Pharmacia Biotech UK Limited, HP79 NA, England, where an enlarged DNA amplification was performed. The reaction volumes for amplification were 25 µL, each containing 1 PCR × buffer with 50 (MgCl2 millimeter carbon chloride), and 10 millilitres of hydrochloric acid (pH 9.0). The mixture was prepared to interact with 15 µL mineral oil and was exposed to the following conditions: 940°C for three minutes, followed by 45 cycles for 1 min. At 94°C for 1 min, at 36°C for 2 min. At 72°C, final for 7 min and extension at 72°C. Amplification products were imaged alongside 1.6% gel DNA markers with TBE × buffer and were detected by adding an ethidium bromide solution for 30 min, and the gels were added in deionized water for 10 min, and they were imaged on a device to document the gel in the presence of UV light.

2.7 Data handling and cluster analysis

Data were recorded and analysed on the computer in the presence of preliminary amplification or lack thereof. If this product was already present in a genetic makeup, it was assigned a value of one (1), and in the absence of a genetic makeup, it was set to zero (0) after excluding the ranges that accept recurrence. Marital comparisons were used from the genotypes, and based on the presence or absence of a unique and common multiple shape product. This was done by renewing similar transactions according to Jacquard (1908). Similarity coefficients were used to create designs for bifurcations, and this was done using a method that was not recommended for use with the UPGMA.

3 Results and discussion

Data in Table 2 show the total number of 27 PCR amplification products as a result of using five different random primers. Over the six wheat varieties, RAPD–PCR showed only five monomorphic bands and 22 polymorphic ones of the total variants, which were not common in all six varieties.

Table 2

The sequence of the RAPD primers used in the study

No. of primerNucleotide sequence (5′ to 3′)
OPAO-03AGTCGGCCCA
OPAO-05TGGAAGCACC
OPAO-07GATGCGACGG
OPAO-08ACTGGCTCTC
OPAO-10GACATCGTCC

These results suggest that these varieties might respond differently in relation to the treatments of drought stress (Bousba et al. 2012). However, it is worth mentioning that the 16 bands that appeared in the banding pattern of Saheel 1 were found in the banding pattern of Sakha 93. Moreover, 15 of the 16 bands of Saheel 1 cultivar had similar amplified bands in the cultivar, Masr 2. These results were confirmed by the dendrogram (Figure 4), where the three varieties, Sakha 93, Saheel 1 and Masr 2, were in the same cluster. In addition, the whole 16 amplified PCR products of the Saheel 1 cultivar were all found to be similar to the Sakha 93 cultivar (Table 3) since both varieties had the highest similarity value (0.86). Table 3 shows that the highest genetic similarity score was observed between Sakha 93 and Saheel 1. Moreover, the lower genetic similarity value (0.83) was observed between the cultivar Sides 1 and Gemmiza 9. Both varieties are in the same cluster (Figure 5). However, Shindwell 1 and Gemmiza 9 had the least genetic similarity, indicating that Shindwell 1 was the most divergent genotype among the six varieties. This result is shown in Figure 1, where Shindwell 1 is located separately in a unique cluster from the other five remaining varieties. In addition, Shindwell 1 had the least total similarity score (3.89) in comparison to the other varieties, assuring its location as a separate cluster (Table 4). This is similar to the findings of Tahir (2008).

Figure 1 Location of the experimental area; Egypt (a), El-Nubaria District (b), NRC Farm (c).
Figure 1

Location of the experimental area; Egypt (a), El-Nubaria District (b), NRC Farm (c).

Table 3

Presence (1) versus absence (0) of PCR-amplified fragments from six wheat varieties using five random primers

NameSides 1Shindwell 1Gemmiza 9Sakha 93Saheel 1Masr 2
Band 1111111
Band 2010001
Band 3111111
Band 4010111
Band 5101111
Band 6000101
Band 7000100
Band 8111110
Band 9111111
Band 10010001
Band 11101111
Band 12111000
Band 13010000
Band 14111111
Band 15111111
Band 16000100
Band 17101111
Band 18100000
Band 19101100
Band 20000101
Band 21010001
Band 22110111
Band 23101111
Band 24010111
Band 25110111
Band 26100111
Band 27100111
Total171512211620
Figure 4 Dendrogram showing the phylogenetic relationships and resulted clusters for the six wheat varieties. 1-Sides 1,2-Shindwell 1,3-Gemmiza 9, 4-Sakha 93, 5-Saheel 1 and 6-Masr 2.
Figure 4

Dendrogram showing the phylogenetic relationships and resulted clusters for the six wheat varieties. 1-Sides 1,2-Shindwell 1,3-Gemmiza 9, 4-Sakha 93, 5-Saheel 1 and 6-Masr 2.

Figure 5 RAPD polymorphism in six wheat species (Sides 1, Shindwell 1, Gemmiza 9, Sakha 93, Saheel 1 and Masr 2). Primer No. 3, Primer No. 5, Primer No. 7, Primer No. 8 and Primer No. 10, respectively.
Figure 5

RAPD polymorphism in six wheat species (Sides 1, Shindwell 1, Gemmiza 9, Sakha 93, Saheel 1 and Masr 2). Primer No. 3, Primer No. 5, Primer No. 7, Primer No. 8 and Primer No. 10, respectively.

Table 4

Genetic similarity values among six wheat varieties in a matrix form

Lane123456
110.550.830.790.850.7
20.5510.50.540.630.67
30.830.510.670.710.56
40.790.540.6710.860.83
50.850.630.710.8610.83
60.70.670.560.830.831
Total4.723.894.274.694.884.59

Unweighted Pair Group Method, with Arithmetic Average (UPGMA) dendrogram of six accessions of releasing varieties of wheat collections, was based on RAPD data. The dendrogram was constructed from the matrix of Dice’s similarity coefficients.

Data in Table 5 show the effect of using different irrigation systems (drip and spray) and different water quantities (natural water 1,200 m3 feddan without water stress, 600 m3 feddan in case of water stress calculated by evapotranspiration ET0 from the data presented in Table 1 and Figure 2), and the effect of using the six genotypes of wheat (Masr 2, Sakha 93, Gemmiza 9, Saheel 1, Sides 1 and Shindwell 1) on grain yield, biomass and water productivity.

Table 5

Effect of irrigation system, water stress, wheat varieties of grain, biomass yield and water productivity

Irrigation system (I)Water amount (II) (m3/fed)Wheat varietiesGrain (kg/fed)Biomass (kg/fed)WP (kg/m3)
DripNormal 1,200Masr 2 (sensitive)2,6496,6362.21
Sakha 93 (resistant)2,8213,9742.35
Gemmiza 9 (sensitive)1,5453,9631.29
Saheel 1 (resistant)1,9266,1591.61
Sides 13,7306,8833.11
Shindwell 12,7764,2942.31
Mean2,575a5,318a2.15d
Stress 600Masr 2 (sensitive)2,6115,8904.35
Sakha 93 (resistant)2,7893,5164.65
Gemmiza 9 (sensitive)1,5134,3562.52
Saheel 1 (resistant)1,8905,1843.15
Sides 13,6966,9996.16
Shindwell 12,7393,9194.56
Mean2,540b4,977b4.23a
Mean2,557e5,148e3.19e
SprinklerNormal 1,200Masr 2 (sensitive)2,2876,0373.81
Sakha 93 (resistant)1,6504,4352.75
Gemmiza 9 (sensitive)1,5123,7742.52
Saheel 1 (resistant)2,8455,2754.74
Sides 12,7364,0774.56
Shindwell 12,0834,0103.47
Mean2,186c4,601c3.64b
Stress 600Masr 2 (sensitive)2,0155,9463.36
Sakha 93 (resistant)1,3774,3442.30
Gemmiza 9 (sensitive)1,2403,6842.07
Saheel 1 (resistant)2,5725,1844.29
Sides 12,4643,9864.11
Shindwell 11,8113,9193.02
Mean1,913d4,511d3.19c
Mean2,049f4,556f3.42f
LSD at 0.0515.822.40.48
LSD at 0.05 of interaction (I) × (II)135.2185.30.14

It is possible to deduce that the grain and biomass yield have taken the same direction as the data, while the water productivity has taken a different direction due to the quantities of water calculated by them. Both mean grain and biomass yield were highest under drip irrigation and the values were 2,557, while the mean values for both were lowest under spray irrigation with values of 2,049 and 4,556 kg/fed, respectively.

In the case of studying the effect of water stress, the mean values of both grain and biomass were obtained under drip irrigation without water stress (2,575 and 5,318 kg/fed, respectively). In case of water stress, the lowest values for both grain and biomass crops were 2,540 and 4,977 kg/fed, respectively. The mean values of both grain yield and biomass were obtained with natural water and without water stress at mean values of 2,186 and 4,601 kg/fed, respectively. In the case of water stress, the lowest values were: 1,913 and 4,511 kg/fed, respectively.

In contrast, the results obtained for water productivity were generally higher with spray irrigation, with a 6% increase compared with drip irrigation. In comparing water productivity under water stress with natural water without water stress, the proportion of increase in water productivity was greater under water stress by 49% under drip irrigation Panama and 12% under spray irrigation, and these ratios are the same proportion of water savings if the use of these items is followed under the same conditions and under water stress by 50% of evapotranspiration ET0.

When comparing the six genotypes under all the previous factors, it can be concluded that the biomass value, grain yield and water productivity were highest using genotype Sides 1, except for water stress with sprinkler irrigation system, while genotype Gemmiza 9 was the lowest in all cases except with normal water without water stress under drip irrigation.

Due to the differences in the characteristics of the six wheat genotypes, with differences in its responses to climatic factors like temperature and the study factors under different irrigation systems and the presence of water stress or normal irrigation water, all of these resulted in significant differences between all six wheat genotypes. This is consistent with previous researches conducted by Goyal and Mansour (2015), Mansour and El-Melhem (2015), Mansour et al. (2019a,b,c), Tayel et al. (2012, 2015, 2018), and Mansour and Aljughaiman (2015).

Figure 6 shows that the drought tolerance index was clearly affected by the irrigation method and also the genetic parameters of the Egyptian wheat varieties used in the current study, where the value of the drought tolerance index under sprinkler irrigation was clearly higher than its value under drip irrigation by more than ten times.

Figure 6 The effect of wheat varieties and irrigation systems on the drought-resistant index.
Figure 6

The effect of wheat varieties and irrigation systems on the drought-resistant index.

As for the varieties, the values also clearly differed between all varieties, and the highest value was recorded under the Gemmiza 9 variety, while the lowest value was recorded under the Saheel 1 variety.

4 Discussion

Water productivity (WP) based on Condon et al. (2002) was assessed for different Egyptian wheat varieties under different irrigation systems (sprinkler and drip) by reducing the use of water to only 40% of the amount normally needed for crop production. The relationship between the irrigation system and drought tolerance in the experimental area (sandy soil) could not be achieved without using suitable irrigation systems, which controlled the amounts of water applied to the different wheat varieties (Mansour et al. 2015a,b; Hellal et al. 2019; Abd-Elmabod et al. 2019).

It has also been well documented that high potential leads to high productivity under conditions of limited water and is generally associated with reduced water consumption (for example, Munoz et al. 1998). Advantages related to the possibility of lower yields, such as the presence of young plants (Martin et al. 1999) because of the short growth period (Munoz et al. 1998), are attributed to the high WP because they reduced water use. Dehydration was also avoided with the improving soil moisture absorption by the roots for low WP in the Egyptian wheat varieties. Other reviews by Mansour et al. (2016) showed more clearly the differences in WP between the genotypes based on the differences in the quantities of water use rather than the differences in the grain yield of those plants.

These measurements clearly demonstrated the importance of stored soil moisture (Mansour et al. 2019a). Therefore, in this case there were absolutely no relationships among the various WPs and maintaining production of plants under shortage of water. In dryland conditions, stored soil moisture is attributed to the advantage of young plants, with moderate growth and short growth periods (Mansour et al. 2019b).

5 Conclusion

The first cluster consisted of two groups. The first group comprised of Sakha 93 (4) and Saheel 1 (5), at a level of 86% genetic similarity. The second group comprised of the closely related species Masr 2 (6), at a level of 83% genetic similarity to group I. The second cluster consisted of one of the groups, which comprised of Gemmiza 9 (3) and Sides 1 (1), at a level of 83% genetic similarity. The third cluster consisted of only one group, comprised of only one species Shindwell 1 (2). The three clusters were like each other at a level of 50% genetic similarity. Genetic similarity ranged between 50% and 86%.

Upon observing the genetic diversity in the analysis of the current study, we can conclude that the technique called RAPD–PCR can estimate the genetic diversity between the different varieties of wheat we had, with the very quick discovery of a huge number of genomes that can bear water shortages. Using this analysis, a description of the various genotypes and genotypes for bread wheat can be made, which helps to know the degree of drought tolerance, when conducting molecular breeding. This allows the early discovery of drought tolerant wheat genotypes for cultivation in high temperature and lower water supply, which occurs in Egypt.

With regard to studying the response of the six genotypes under all factors of the study, it can be concluded that the biomass value, grain yield and water productivity were highest using genotype Sides-1 in all cases, except for water stress with sprinkler irrigation system, while genotype Gemmiza 9 was the lowest in all cases, except with normal water without water stress under drip irrigation.

  1. Conflict of interest: Authors declare no conflict of interest.

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Received: 2019-08-16
Revised: 2020-04-02
Accepted: 2020-04-17
Published Online: 2020-07-08

© 2020 Hani A. Mansour et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  3. The Analysis of Nutritional Habits of the Third Age Students in Poznań
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https://doi.org/10.1515/opag-2020-0030
Schlagwörter für diesen Artikel
wheat genotypes; morphology; DNA (RAPD); irrigation systems; water productivity
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Synergistic and antagonistic effects of soil applied P and Zn fertilizers on the performance, minerals and heavy metal composition of groundnut
  3. The Analysis of Nutritional Habits of the Third Age Students in Poznań
  4. Biochar, poultry manure and NPK fertilizer: sole and combine application effects on soil properties and ginger (Zingiber officinale Roscoe) performance in a tropical Alfisol
  5. Nutritional and functional properties of maize-oyster mushroom (Zea mays-Pleurotus ostreatus) based composite flour and its storage stability
  6. Rural Folks Perception of Suicide Drivers in rural communities of Delta State, Nigeria: Implications for Societal and Agricultural Security
  7. Evaluation of Potato (Solanum tuberosum L.) Varieties for Yield and Some Agronomic Traits
  8. Micropropagation, Callus Induction and Regeneration of Ginger (Zingiber officinale Rosc.)
  9. The impact of foliar feeding on the yield components of three winter rape morphotypes (Brassica napus L.)
  10. The effect of soil water content and biochar on rice cultivation in polybag
  11. Influence of Azospirillum brasilense associated with silicon and nitrogen fertilization on macronutrient contents in corn
  12. Effect of Christmas Island rock phosphate and rice straw compost application on soil phosphorus availability and maize (Zea mays L.) growth in a tropical acid soil of Kelantan, Malaysia
  13. The evolution of Romanian agritourism and the role of European Union subsidies in rural areas
  14. Assessment of crop farmers coping strategies to pastoralism/nomad activities in Nigeria
  15. Assessing yield stability in African yam bean (Sphenostylis stenocarpa) performance using year effect
  16. Walnut leaf extract-based green synthesis of selenium nanoparticles via microwave irradiation and their characteristics assessment
  17. Evaluating the water productivity by Aquacrop model of wheat under irrigation systems and algae
  18. Micronutrients status of mango (Mangifera indica) orchards in Multan region, Punjab, Pakistan, and relationship with soil properties
  19. Molecular studies for drought tolerance in some Egyptian wheat genotypes under different irrigation systems
  20. Biosynthesized silver nanoparticles using Ulva lactuca as a safe synthetic pesticide (in vitro)
  21. Development of SPE clean-up procedure for acrylamide determination from potato-based products by GC-MS/MS
  22. Insulative effect of plastic mulch systems and comparison between the effects of different plant types
  23. Trends and constraints in the production and utilization of cowpea leaves in the arid and semi-arid lands of Kenya
  24. Microbiome of abandoned agricultural and mature tundra soils in southern Yamal region, Russian Arctic
  25. Method of biochar application affects growth, yield and nutrient uptake of cowpea
  26. Identity gaps and negotiations among layers of young farmers: Case study in Indonesia
  27. Genetically modified crops in India: Experiments with Bt Cotton to explore the road ahead
  28. Effect of phyto-extracts of neem (Azadirachta indica) and garlic (Allium sativum) on leaf spot disease of groundnut (Arachis hypogaea L.)
  29. Influence of cellulose fibers on physicochemical properties of biodegradable films based on polysaccharide derivatives
  30. Do the determinants of food security differ in improved rice variety adoption? Evidence from Sierra Leone
  31. Assessment of yield components of some cassava (Manihot esculenta Crantz) genotypes using multivariate analysis such as path coefficients
  32. Microbiome of post-technogenic soils of quarries in the Republic of Bashkortostan (Russia)
  33. Occurrence of arbuscular mycorrhizal fungi spores in soils of some legumes and their response to varying concentrations of phosphorus application
  34. Baseline fertility status of a gravelly Alfisol in a derived savannah agro-ecological zone of Nigeria
  35. Effect of strain, sex and slaughter weight on growth performance, carcass yield and quality of broiler meat
  36. Palm oil plantation and cultivation: Prosperity and productivity of smallholders
  37. Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad
  38. Classification and geovisualization process of soil data using a web-based spatial information system
  39. Assessment of stakeholders’ contributions to livestock development in Delta State, Nigeria: Rural infrastructure intervention
  40. Linear and structural changes in rural space – the positive and problematic aspects (case of Latvia)
  41. Farmers’ responses to organic rice farming in Indonesia: Findings from central Java and south Sulawesi
  42. Interactive effects of Potato virus Y and Potato leafroll virus infection on potato yields in Uganda
  43. Adoption of agricultural practices with climate smart agriculture potentials and food security among farm households in northern Nigeria
  44. Green approach in gold, silver and selenium nanoparticles using coffee bean extract
  45. Value chain for agritourism products
  46. Trichoderma asperellum affects Meloidogyne incognita infestation and development in Celosia argentea
  47. Growth, chlorophyll content and productivity responses of maize to magnesium sulphate application in calcareous soil
  48. Gene action in low nitrogen tolerance and implication on maize grain yield and associated traits of some tropical maize populations
  49. Zambian farm blocks: A vehicle for increased private sector investments
  50. Farm size and its impact on land use: The case of the South African private wildlife industry
  51. Characterizing macropore structure of agrosoddy-podzolic soil using computed tomography
  52. Influence of priming on germination, development, and yield of soybean varieties
  53. Review Articles
  54. Zinc biofortification as an innovative technology to alleviate the zinc deficiency in human health: a review
  55. Biological management of allelopathic plant Parthenium sp.
  56. Microbial antagonists against plant pathogens in Iran: A review
  57. Communication
  58. Risk assessment of Ni, Cr, and Si release from alkaline minerals during enhanced weathering
  59. Phylogenetic relationships among accessions in Citrus and related genera based on the insertion polymorphism of the CIRE1 retrotransposon
  60. Gamma radiosensitivity study on wheat (Triticum turgidum ssp. durum)
  61. Special Issue on the 10th Circumpolar Agriculture Conference 2019
  62. Preface to the Special Issue on the 10th Circumpolar Agriculture Conference 2019
  63. Rumen bacterial community of young and adult of reindeer (Rangifer tarandus) from Yamalo-Nenets Autonomous District of Russia
  64. Carcass weight of Greenlandic lambs in relation to grazing area biomass
  65. Transnational cooperation to develop local barley to beer value chains
  66. Addition of biochar to acidic boreal podzolic soils enhances micronutrient availability and crop productivity
  67. Digitalisation in a local food system: Emphasis on Finnish Lapland
  68. Abandoned agricultural soils from the central part of the Yamal region of Russia: morphology, diversity, and chemical properties
  69. Fishing as an important source of food in the Arctic and subarctic zones of Yakutia
  70. Special Issue on the APA 2019 - 11th Triennial Conference
  71. Yield potential of sandponically produced sweetpotato (Ipomoea batatas (L.) Lam) pre-basic seed for selected genotypes
  72. Monitoring with social media: Experiences from “integrating” WhatsApp in the M&E system under sweet potato value chain
  73. Breeding farmer and consumer preferred sweetpotatoes using accelerated breeding scheme and mother–baby trials
  74. Decentralised sweetpotato (Ipomoea batatas) vine multiplication in Lake Zone, Tanzania: Five years later
  75. Mineral fertilizer use in land-scarce conditions: Case of Rwanda
  76. Assessment of the roles and farmer-preferred traits of sweetpotato in a crop-livestock farming system in Rwanda: implications for breeding dual-purpose varieties
  77. A public–private partnership to speed up potato breeding
  78. Dietary acrylamide intake by potato crisps consumers: A case of Nairobi County
  79. Special Issue on ICESAT 2019
  80. Biochemical activity and bioassay on maize seedling of selected indigenous phosphate-solubilizing bacteria isolated from the acid soil ecosystem
  81. Identifying best parameters of particleboard bonded with dextrin-based adhesives
  82. Performance of rice paddy varieties under various organic soil fertility strategies
  83. Improved strength properties of LVL glued using PVAc adhesives with physical treatment-based Rubberwood (Hevea brasiliensis)
  84. Evaluation and possible mechanism of beet armyworm (Spodoptera exigua Hubner) resistance to chlorpyrifos and their sensitivity to neem oil insecticides
  85. Fermented compost and N-fertilizer for enhancing the growth and productivity of purple eggplant on vertisols
  86. Bioactive peptide effect on brain activity identified by 2D brain mapping
  87. Shifting of microbial biodiversity and soil health in rhizomicrobiome of natural forest and agricultural soil
  88. Special Issue on CERNAS 2020
  89. Evaluation of consumer habits and knowledge about dietary fibre and fibre-rich products in Turkish population
  90. Evaluation of texture in jelly gums incorporating berries and aromatic plants
  91. Food choice motivations and perception of a healthy diet in a developing Mediterranean country
  92. The evolution of the milk sector in Portugal: Implications from the Common Agricultural Policy
  93. Energetic and exergetic analysis of a convective drier: A case study of potato drying process
  94. Cluster analysis to the factors related to information about food fibers: A multinational study
  95. Study of the drying kinetics and calculation of mass transfer properties in hot air drying of Cynara cardunculus
  96. Short food supply chains – a growing movement. The case study of the Viseu Dão Lafões Region
  97. Evaluation of phenolic compounds and antioxidant activity in some edible flowers
  98. Chemical composition and study on liquefaction optimization of chestnut shells
  99. Is organic agriculture a potential public health indicator? Evidence from literature
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