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Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

The nitrogen regulatory policy (NRP) solution is introduced as a mitigation measure against environmental nitrogen losses and keeps food production in the Safe Operating Space of the Nitrogen Planetary Boundary. Meanwhile, scientific research shows that steps taken to reduce environmental harm can increase the unpredictability of calorie production from crops. This study sought to investigate the impact of NRP solutions on the level of risk of accessibility to calorie sources from domestic production, the variations in calorie sources by livestock and non‐livestock diet components, and the responses of different dietary preferences, namely, poor, medium, and rich livestock protein diets, against NRP solutions in the Zayandeh‐Rud River basin, Iran. We developed the aggregate household food security index (AHFSI) and combined it with outputs of crop simulation model to examine how changes in dietary energy supplies under three NRP scenarios—low, moderate, and high nitrogen fertilizer application—affect the stability of three regional dietary preferences. The comparison of NRP scenarios movements realized that increases (or decreases) in nitrogen fertilizer rates contradicted the stability in AHFSI. Additionally, a one‐unit change in the average calories from non‐livestock sources, such as wheat and potatoes, results in greater fluctuations in the standard deviations of produced calories compared to changes in meat and dairy production. We proposed that in order to prevent adverse effects of NRP solutions on food security, mitigation strategies addressing the NRP solution should be structured based on (i) regional heterogeneities, (ii) type of crops, that is, food and feed crops, (iii) the range of nitrogen rates movement; (iv) and the socioeconomic background related to dietary preferences or economic deciles of food expenditure. [ABSTRACT FROM AUTHOR]

This study aims to predict the yield of two strategic crops in Iran; wheat and rice, under climate scenarios that indicate probable changes in climate variables. It implemented by a stochastic model based on the Monte Carlo method. This model were estimated based on historical data from 1988 to 2019 for precipitation and temperature provided possible changes in the pattern of and their probability of occurrence. The results show that rain-fed wheat, irrigated wheat and rice yields decrease by 42%, 29% and 21% respectively in the average scenario. Therefore, policy makers should try to make the right decisions to increase the production of the country's strategic crops. R&D management to introduce drought-tolerant varieties, use of modern irrigation systems and use of low-volume irrigation methods are some of the proposed solutions to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

Salinity is challenging agriculture by reducing crop productivity. Endophytes from halophyte plants have the potential to alleviate salinity stress in crops. Here, 78 bacterial strains were recovered from 10 habitat-adapted halophyte plants in the central deserts of Iran. Eight superior halotolerant strains tolerating 3 M NaCl, were identified as the Gram-positive genera Bacillus and Oceanobacillus (Bacillaceae), Brachybacterium (Dermabacteraceae), Micrococcus (Micrococcaceae), and Salinicoccus (Staphylococcaceae). Scanning electron microscopy (SEM) showed that the most halotolerant strain, i.e., Salinicoccus hispanicus, experienced accelerated binary fission under salinity. Furthermore, seeds of the two winter wheat cultivars, i.e., cv. Homa (rain-fed) and cv. Mihan (irrigated), inoculated by those bacteria, showed enhanced germination indices under different salinity gradients, and also under normal conditions. Indeed, the superior bacteria, i.e., Oceanobacillus sp. strain 76, Micrococcus luteus strain 14, and Bacillus sp. strain 7, enhanced seed germination indices in both wheat cultivars including final seed germinations, as well as the root, shoot and seedling lengths. These strains were capable of producing the plant hormone auxin (IAA). Thus, we propose that halotolerant endophytic bacteria from desert-adapted halophyte plants could alleviate salinity stress in strategic crops such as the wheat Triticum aestivum L., especially in dryland farming. [ABSTRACT FROM AUTHOR]

Wheat dwarf virus (WDV) has caused considerable economic loss in the global production of grain crops. Knowledge of the evolutionary biology and population history of the pathogen remain poorly understood. We performed molecular evolution and worldwide phylodynamic analyses of the virus based on the genes in the protein-coding region of the entire viral genome. Our results showed that host-driven and geography-driven adaptation are major factors that affects the evolution of WDV. Bayesian phylogenetic analysis estimates that the average WDV substitution rate was 4.240 x 10-4 substitutions/site/year (95% credibility interval, 2.828 x 10-4-5.723 x 10-4), and the evolutionary rates of genes encoding proteins with virion-sense transcripts and genes encoding proteins with complementary-sense transcripts were different. The positively selected sites were detected in only two genes encoding proteins with complementary-sense, and WDV-barley are subject to stronger purifying selection than WDV-wheat. The time since the most recent common WDV ancestor was 1746 (95% credibility interval, 1517-1893) CE. Further analyses identified that the WDV-barley population and WDV-wheat population experienced dramatic expansion-decline episodes, and the expansion time of the WDV-barley population was earlier than that of the WDV-wheat population. Our phylogeographic analysis showed that the WDV population originating in Iran was subsequently introduced to Europe, and then spread from Eastern Europe to China. [ABSTRACT FROM AUTHOR]

• The wheat traits and nitrogen status retrieved from the Landsat-7 images. • Three algorithms are compared to predict wheat parameters and nitrogen status. • The ANN and SVM algorithms can effectively predict plant nitrogen percentage. • The DNN algorithm can predict the wheat parameters better than SVM and ANN. Monitoring wheat biophysical variables during the growing season is essential for achieving precision agriculture's important goals. This study estimated plant height, leaf development stage, leaf area index, dry matter, and plant nitrogen content in wheat from Landsat-7 images in North Iran using machine learning algorithms. Forty points were selected from ten wheat fields (270 ha), and the agronomic parameters were recorded during the 2011–2012 growing season. The machine learning models applied in this study were Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Neural Network (DNN). Results suggest that the DNN model was the best for predicting all the parameters, except for the nitrogen percentage, in which SVM and DNN have the same accuracy. The DNN model R2 was 0.82, 0.95, 0.95, and 0.90 RMSE (Root Mean Square Error) 9.61, 0.46, 0.47 and 1.2 and MAE (Mean Absolute Error) 26.4, 0.34, 0.28, and 0.84 for estimating plant height, leaf area index, leaf development stage and dry matter biomass, respectively. The DNN and SVM algorithms can effectively predict plant nitrogen percentage with identical R2, RMSE, and MAE values of 0.83–0.84, 0.44–0.46, and 0.33–0.36, respectively. These results show that machine learning models have great potential to provide timely and accurate data for better wheat management under field conditions from satellite imagery. [ABSTRACT FROM AUTHOR]

Rising temperatures of 1.5–4 °C by the end of the twenty-first century and continuing after 2100 have caused climate change to threaten food security. Indeed, the rainfed production of strategic crops such as wheat in countries such as Iran, which are likely to be highly affected by climate change, is very important. This study aimed to identify the suitable areas for rainfed wheat cultivation and the yield gap estimation in current and future climatic conditions using geographical analysis, a crop simulation model, and the GYGA protocol in Iran. For this, environmental variables (including average, minimum, and maximum temperatures, rainfall) along with topographic (slope, elevation) and edaphic components (such as organic carbon, pH, EC, [CaCO3], exchangeable sodium percentage, cation exchange capacity, and soil erosion) were involved. The results revealed that around 30% of the total areas (6.62 million ha) are not suitable at all, while just 13% of total lands (2.97 million ha) were highly suitable and suitable. These areas are mainly located in the northern part of Iran. The study of the climate change scenarios indicated that the northern, northwestern, and western parts of the country have great potential for rainfed wheat production and future climate change will benefit wheat production in these areas, but in other parts of the country, more unfavorable conditions are expected. The mean water-limited potential yield (Yw) difference between marginally suitable to not suitable classes was narrow (< 0.3 t ha−1), while for highly suitable to moderately suitable classes was about 2.5 t ha−1. [ABSTRACT FROM AUTHOR]

As a strategic crop, wheat is important to eht world's food supply. Salinity is a major threat to the food supply in the whole world and Iran. The research was carried out for identifying morpho-physiological and biochemical markers to select salt-tolerant wheat genotypes. The research was done on two salt-tolerant (Pishgam) and susceptible (Shahryar) bread wheat cultivars under saline conditions (250 mM NaCl). The results showed a high genetic diversity for most traits. The traits of harvest index, root-to-shoot dry weight ratio, root dry weight, plant height, potassium-to-sodium ion ratio, total protein, superoxide dismutase, catalase, peroxidase, and total carbohydrates showed the highest alignment with increasing grain yield under salt stress conditions. The traits of potassium ion accumulation (K+), superoxidase dismutase (SOD), proline (Pr), and relative water content (RWC) were entered into the regression model, as the most important traits affecting grain yield under salinity conditions, respectively. According to the results, it is possible to suggest two groups of markers to select salt-tolerant genotypes. The first group includes some morpho-physiological markers namely a high amount of harvest index, main spike weight, relative water content, water consumption, root-to-shoot dry weight ratio, total carbohydrates, proline, and root dry weight; and the second group includes the ionic and biochemical markers namely a high amount of K+ accumulation, K+/Na+ accumulation ratio, SOD, catalase, peroxidase, and a low amount of Na+ accumulation, malondialdehyde, and hydrogen peroxide. Therefore, the aboveintroduced markers can be useful indicators to select salt-tolerance genotypes in future wheat breeding programs. [ABSTRACT FROM AUTHOR]

Through a survey that was conducted from 2020 to 2021, specimens were collected from wheat fields (Triticum aestivum L.) in West Azarbaijan province in the northwest of Iran using a sweep net. These are Neotrichoporoides nyemitawus (Rohwer, 1921), a new report for Iranian fauna and the West Palaearctic subregion. Illustrations of N. nyemitawus are provided, and its morphological characters are discussed. Neotrichoporoides Girault, 1913 in Iran, is represented by two species, N. szelenyii (Erdös, 1951) and N. viridimaculatus (Fullaway, 1955). Hereby, an identification key for Iranian species was provided. A worldwide distribution map of N. nyemitawus is also presented. Its role as the most important biocontrol agent of the shoot flies, Atherigona spp. (Diptera: Muscidae) was discussed. [ABSTRACT FROM AUTHOR]

Investigating the relationship between grain yield and main agronomic traits and understanding the trend of changes in traits during breeding history are important for breeding strategies. In order to determine the trend of changes in improved wheat cultivars and calculate the genetic gain of yield and related traits, 20 spring wheat cultivars released for the northern climate of the Iran during the years 1968 to 2018 were evaluated. Cultivars were cultivated in a randomized complete block design with three replications in Gorgan, Gonbad, Sari and Moghan in three consecutive years (2015–18). Morphological and phenological traits and grain yield and related traits were assessed in each year. Linear regression analysis between investigated characteristics and year of cultivar release showed that the grain yield, biological yield, harvest index, number of spikes per square meter, thousand-kernel weight and grain filling rate were significantly increased. No significant changes, however, were observed during breeding programs in maturity-related traits such as days to heading, days to anthesis, days to maturity, duration of grain filling, plant height, peduncle length, spike length and number of grains per spike. According to the positive and significant correlation between biomass, harvest index, number of spikes per square meter and thousand-kernel weight with grain yield, it seems that breeders in the northern climate of the country have been selected these traits to increase grain yield during the process of breeding cultivars. [ABSTRACT FROM AUTHOR]

Background: Durum wheat is one of the most important crops, especially in the Mediterranean region. Insight into the genetic diversity of germplasm can improve the breeding program management in various traits. This study was done using single nucleotide polymorphisms (SNP) markers to characterize the genetic distinctiveness and differentiation of tetraploid wheat landraces collected from nine European and Asian countries. A sum of 23,334 polymorphic SNPs was detected in 126 tetraploid wheat landraces in relation to the reference genome. Results: The number of identified SNPs was 11,613 and 11,721 in A and B genomes, respectively. The highest and lowest diversity was on 6B and 6 A chromosomes, respectively. Structure analysis classified the landraces into two distinct subpopulations (K = 2). Evaluating the principal coordinate analysis (PCoA) and weighted pair-group method using arithmetic averages (WPGMA) clustering results demonstrated that landraces (99.2%) are categorized into one of the two chief subpopulations. Therefore, the grouping pattern did not clearly show the presence of a clear pattern of relationships between genetic diversity and their geographical derivation. Part of this result could be due to the historical exchange between different germplasms. Although the result did not separate landraces based on their region of origin, the landraces collected from Iran were classified into the same group and cluster. Analysis of molecular variance (AMOVA) also confirmed the results of population structure. Finally, Durum wheat landraces in some countries, including Turkey, Russia, Ukraine, and Afghanistan, were highly diverse, while others, including Iran and China, were low-diversity. Conclusion: The recent study concluded that the 126 tetraploid wheat genotypes and their GBS-SNP markers are very appropriate for quantitative trait loci (QTLs) mapping and genome-wide association studies (GWAS). The core collection comprises two distinct subpopulations. Subpopulation II genotypes are the most diverse genotypes, and if they possess desired traits, they may be used in future breeding programs. The degree of diversity in the landraces of countries can provide the ground for the improvement of new cultivars with international cooperation. linkage disequilibrium (LD) hotspot distribution across the genome was investigated, which provides useful information about the genomic regions that contain intriguing genes. [ABSTRACT FROM AUTHOR]

The increase in population and decrease in agricultural land per capita requires intelligent management to increase production per unit area and higher productivity of land use to ensure food security. In this regard, evaluation of soil fertility and productivity is a prerequisite for sustainable agricultural development. The present study sought to investigate the soil fertility status of GIS-based Fuzzy-AHP(FAHP) techniques to assess the soil fertility status of wheat crops in Shadkam plain of Fars province. For this purpose 210 study samples from topsoil were collected and the most important chemical parameters affecting soil fertility were analyzed. According to the results, the most important limiting characteristics were OC, available phosphorus and soil pH. The application of FAHP method determined 4130.01 ha, 10.82% of land very high fertility class, 7615.30 ha, 19.95% high fertility class, 23942.30 ha, 62.77% moderately fertility class, and 2452.10 ha, 6.42% low fertility class. Thus, the fuzzy-AHP model is found to be of high accuracy for predicting soil fertility in the study area. Overall, it can be concluded that the preparation of a soil fertility map separately can be an effective tool and first step in soil fertility studies and the optimal use of fertilizers. [ABSTRACT FROM AUTHOR]

One of the most devastating foliar diseases of wheat worldwide is Septoria leaf blotch (STB), caused by Mycosphaerella graminicola (asexual stage/Anamorph: Septoria tritici) which has been recently intensified in some regions in Iran. In this study, 49 wheat genotypes and 20 wheat differential genotypes were evaluated for their reaction to infection by six isolates of M. graminicola collected from infected fields during 2016–2017 at seedling stage under greenhouse conditions. According to the analysis of variance (ANOVA) of leaf pycnidia coverage percentage, a significant difference (p <.01) was observed between M. graminicola isolates and wheat cultivars. The interaction between genotypes and isolates was also significant (p <.01) and the results indicated a specific interaction between genotypes and isolates. The results presented Dezful and West Azerbaijan isolates that were the most virulent with more pathogenesis on differential genotypes. Although 47 of the wheat genotypes were susceptible to all isolates, some genotypes, including Wc‐46,224 (Austria), Wc‐45,425 (Portugal), Wc‐45,565 (Turkey), P.S.No4 (Italy), Dehdasht, M3 Synthetic, KavKaz‐k4500, Arina, Flame, and Riband were resistant to all isolates. In addition, the isolates exhibited different virulence patterns on wheat genotypes. The results of this study revealed high virulence of M. graminicola isolates, and Iranian and foreign wheat genotypes, commonly used in the region, presented high susceptibility, and the resistance sources had been identified among genotypes that can be applied in the wheat breeding programs. [ABSTRACT FROM AUTHOR]

Agricultural extension is a key policy to accomplish sustainable agricultural development by improving farmers' knowledge. Agricultural Extension Model Sites (AEMSs) is a new agricultural extension approach for empowering farmers in Iran. Therefore, the purpose of this research is the economic effect assessment of extension services of AEMSs for irrigated wheat production in Iran. Surveys were conducted with 180 people of the main farmers from irrigated wheat AEMSs throughout Iran. The research tool was a questionnaire, the validity of which was examined using the opinions of a group of experts from the Agricultural Education and Extension Institute in Iran. Data analysis was done using econometric analysis and One-way ANOVA analysis through SHAZAM11 and SPSS27 software. Results showed that the extension services significantly affected irrigated wheat yield with an average increase of 0.66 t/ha. Based on the results, out of the Marginal product value ($69 USD) of the extension services provided to the main farmers, 13.3% ($9 USD) had been spent as the cost of extension services and 86.7% ($60 USD) had been net profit gained by farmers. Farmers' behaviors changed as a result of the AEMs with reduced input costs for growing the crop and increased production and profit from the crop. [ABSTRACT FROM AUTHOR]

Puccinia striiformis Westend. f. sp. tritici Eriksson (Pst), the causal agent of stripe rust, is known as an important and destructive pathogen that causes critical wheat losses in many parts of the world. Employing effective resistant genotypes is a safe and operative strategy to combat the disease. Iran is located in the Fertile Crescent, the co-epicentre of wheat and its biotic stresses. Iranian wheat landraces may carry important sources of resistance against rust diseases. Adult plant reactions of 467 Iranian wheat landraces to stripe rust were studied in two cropping seasons in Fars province. One hundred and six landraces showed acceptable levels of resistance and were selected for further seedling and molecular studies based on their lower infection type, coefficient of infection (CI) and average coefficient of infection (ACI). The presence of Yr4, Yr10, Yr24 and Yr32 resistance genes were postulated using 10 different Pst isolates including PstS10, PstS7, PstS3, PstS2+V27, PstS11, PstS8, PstS13, PstS6, PstS0 and Me2018 under greenhouse conditions. Field and greenhouse reactions of near-isogenic lines reconfirmed the presence of the studied Yr resistance genes in 20 genotypes. Moreover, Xcfb3530150, Xpsp3000, Xgwm11, and Xwmc198 SSR markers were employed to identify and validate the presence of the above corresponding genes. The presence of Yr4, Yr10, Yr24 and Yr32 resistance genes in five (1.07%), 10 (2.14%), five (1.07%) and seven (1.5%) experimental landraces were confirmed, respectively. Six genotypes are harbouring more than one resistance gene. These effective resistance sources can be employed in national and international breeding programs. [ABSTRACT FROM AUTHOR]

Managing pathogen damage in wheat production is important for sustaining yields. Fungal plant pathogen genomes encode many small secreted proteins acting as effectors that play key roles in the successful colonization of host tissue and triggering host defenses. AvrStbó is the first described Zymoseptoria tritici avirulence effector, which triggers SrM-mediated immunity in the wheat host in a gene-for-gene manner. Evasion of major resistance factors such as Stbó challenges deployment decisions on wheat cultivars. In this study, we analyzed the evolution of the AvrStbó effector in Iranian isolates of Z. tritici. In total, 78 isolates were isolated and purified from 30 infected wheat specimens collected from the East Azerbaijan and Ardabil provinces of Iran. The pathogenicity of all isolates was evaluated on the susceptible wheat cultivar 'Tajan'. A subset of 40 isolates were also tested for pathogenicity on the resistant cultivar 'Shafir' carrying Stb6. Genetic diversity at the AvrStbó locus was analyzed for 14 isolates covering the breadth of the observed disease severity. The AvrStbó sequence variation was high, with virulent isolates carrying highly diverse AvrStbó haplotypes. In an analysis including more than 1,000 additional AvrStbó sequences from a global set of isolates, we found that virulent isolates carried AvrStbó haplotypes eitherclustering with known virulent haplotypes on different continents or constituting previously unknown haplotypes. Furthermore, we found that AvrStbó variants from avirulent isolates clustered with known avirulent genotypes from Europe. Our study highlights the relevance of AvrStbó for Z. tritici virulence and the exceptional global diversity patterns of this effector. [ABSTRACT FROM AUTHOR]

Three types of tillage implement including moldboard plow (conventional tillage), heavy disk (tandem disk harrow) and chisel plow (minimum tillage), is common in Iran and widely used by farmers for plowing and preparing the land for both rain-fed and irrigated wheat production. A lot of chemical and physical properties of the soils are influenced by the type of tillage. Tillage methods are usually evaluated by the amount of oxygen in the soil and distribution of organic matter. On the other hand, using the cropping operations program can significantly affects the amount of oxygen diffusion rate (ODR), fuel consumption and energy efficiency. Prediction of the accurate amount of fuel requirements for tillage operation of the soil is difficult. Therefore, in the current research, the effects of abovementioned tillage methods on the amount of power required, fuel consumption, oxygen diffusion rate and some soil physical properties including bulk density, and organic carbon contents in a randomized complete block design was studied. The results showed that minimum tillage methods increase the amount of organic matter, reduces fuel consumption and needed less power. Highest fuel consumption of 49.6 liters per hectare and the lowest 21.3 liters per hectare, related to the moldboard plow and heavy disk, respectively. The experimental results showed that the highest ODR was recorded at the moldboard plow, (53.6x10-8 g cm-2 min-1) and the lowest (35x10-8 g cm-2 min-1) measured by heavy disk. This value was equal to 42.5x10-8 g cm-2 min-1 for chisel plow. Mean values of soil organic carbon related to the moldboard plow, chisel plow and heavy disk were 0.38, 0.43 and 0.48 percent, respectively. It can be concluded that by considering the land conditions in Iran, the current research findings recommend the use of chisel plow or heavy disk for rainfed or irrigated wheat production. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) caused mainly by Fusarium graminearum, is a devastating disease of wheat and other small grain cereals in humid and semi-humid areas. Understanding the host-pathogen interaction and realizing the relationship between the host and pathogen is an important issue in breeding programs. In this study, the interaction between F. graminearum isolates causing FHB and wheat genotypes was investigated by inoculating two representative isolates of the fungus from each of Canada, Iran, and CIMMYT, Mexico on five wheat genotypes with different levels of resistance to FHB from each of the mentioned origins. Results showed significant differences in aggressiveness of Fusarium isolates inoculated on wheat genotypes. While CIMMYT isolates produced the least disease severities on wheat genotypes tested (8.1–10.0%), the isolates from Canada and Iran severely infected the genotypes (43.2–59.3%). Significant differences were also observed in reaction of wheat genotypes to Fusarium isolates, and four genotypes including 93FHB37 (Canada), SHA3/CBRD (CIMMYT), N-83-6 (Iran), and NG8675/NING8645 (CIMMYT), with disease severities from 8.0 to 9.0%, were consistently among the most resistant ones. Our results also showed significant interactions (P < 0.0001) between Fusarium isolates and wheat genotypes used. Due to significant differences in disease rate caused by Fusarium isolates in wheat genotypes, it seems that all isolates, regardless of their rate of aggressiveness, will be able to differentiate wheat genotypes in terms of resistance/susceptibility. However, in wheat breeding programs, the criteria used for selecting resistant genotypes, have to be considered in appropriate to the rate of aggressiveness of the isolate used. [ABSTRACT FROM AUTHOR]

This study is aimed to compare the hydrophysical properties of Cambisols and wheat yield on plots under different soil management strategies. The soil (0–20 cm) consists of 21% sand, 37% silt, and 42% clay. This experiment was conducted with three tillage system types (TST) in main plots (reduced tillage using combined tillage: T1, reduced tillage comprised of chisel plough: T2 and conventional tillage: T3), three harvest residue covers (HRC) in sub-plots (no residue: C1, 40% wheat residue: C2, and 80% wheat residue: C3), and three implement forward speeds (IFS) in sub-subplots (4 km/h: S1, 7 km/h: S2, and 10 km/h: S3). Infiltration tests were performed by single-disc tensions infiltrometer with a diameter of 20 cm at successive applied matric suction (h) of 20, 14, 4, 1, and 0 cm. Mean comparison results showed that soil organic matter decreased in T3 treatment, due to soil aggregate degradation. T3, C3, and S3 treatments increased the hydraulic conductivity at matric suction (h) of 1 cm by increasing the number of macropores per unit area and their contribution to the saturated water flux. In contrast, T1 treatment increased hydraulic conductivity at h of 4, 14, and 20 cm by increasing the number of meso- and micropores. The contribution of micropores to the saturated water flux, hydraulic conductivity at h = 20 cm were higher in residual-cover-free plots. Hydraulic conductivity at h of 1, 4, and 14 cm increased with increasing HRC due to an increase in the number of macro- and mesopores and their contribution to the saturated water flux. Macroscopic capillary length () under T1, T2 and S1 was more than those under T3 and high IFS. T3 treatment also reduced likely due to its destruction effects on pore continuity. The application of T2, C2, and S2 treatments led to maximum wheat yield, likely by minimizing mechanical manipulation, reducing evaporation and improving soil moisture conditions. [ABSTRACT FROM AUTHOR]

Aegilops tauschii is considered as an important gene source for improving common wheat, which has wide distribution and rich genetic variation. In order to understand genetic diverstity and population structure of A. tauschii from different origins, ISSR markers were used to evaluate genetic diversity and population structure of 56 A. tauschii accessions. The results were as follows: (1) The 170 polymorphic bands were detected by 16 ISSR primers, and polymorphic bands of each ISSR primer ranged from 3 to 18, with an average of 10.63. The variation of polymorphism information content (PIC) ranged from 0.17 to 0.85, with an averaged of 0.67. (2) The comparison among four populations indicated that the population genetic diversity of Central Asia was the highest (He=0.225 4, I=0.355 7) and gene flow between populations was relatively low (Nm=1.638 6) . (3) The clustering results showed that 56 A. tauschii accessions were divided into two groups at the genetic similarity coefficient 0.67, of which eight accessions from Tajikistan and Turkmenistan were clustered in Group 2. And, the Group 1 including 48 accessions could be further divided into three sub-groups, which indicated that A. tauschii accessions with clustering together have the same origin. (4) Based on population structure analysis, 56 A. tauschii accessions were divided into five populations, of which the V population from Iran in West Asia had relatively consistent genetic background and relatively low degree of hybridization. Furthermore, the Q value analysis of populations showed that the genetic relationship of IV population were relatively complex, producing the most abundant genetic diversity. The results of this study can provide an important reference for analysis of genetic relationship, protection of biodiversity, and lay a foundation for the scientific utilization and evolution research of A. tauschii. [ABSTRACT FROM AUTHOR]

In order to evaluate the quantitative and qualitative performance of spring bread wheat cultivars under different planting dates, an experiment was conducted in Gonbad Agricultural Research Station, Gonbad, Golestan province, Iran, during two cropping seasons (2020-2022) as split plot based on randomized complete block design (RCBD) with four replications. Seven planting dates (1 November, 11 November, 21 November, 1 December, 11 December, 21 December and 31 December) were placed in main plots and four bread wheat genotypes (Araz, Arman, Taktaz and N-93-9) were placed in subplots. The delayed planting led to a significant decrease in the length of the phenological stages and the grain filling period. The highest grain yield was obtained from the planting at 11 November (5813 kg ha-1 ), which was not significantly different from grain yield in 21 November (5788 kg ha-1 ) and 31 November (5641 kg ha-1 ) planting dates. In most of the agronomic traits, the delayed planting dates (21 December and 31 December) had significantly lower values than planting in 11 November, 21 November and 1 December. Mean comparison for interaction effect of planting date×cultivar showed that planting Arman cultivar in 11 November, Araz cultivar in 11 November, 21 November and 1 December, Arman cultivar in the 21 November and Taktaz cultivar in the 21 November led to the highest grain yield. In the three delayed planting dates (11 December, 21 December and 31 December), the grain yield of Taktaz and Araz cultivars was higher than other genotypes. Mean comparisons on the traits related to bread-making quality showed that the two delayed planting dates (21 December and 31 December) had higher values in terms of protein and wet gluten content, compared to the two early planting dates (1 November and 11 November). Taktaz cultivar had the highest bread-making quality, particularly because it indicated the highest protein and wet gluten content. [ABSTRACT FROM AUTHOR]

Background: Wild relatives of wheat are one of the most important genetic resources to use in wheat breeding programs. Therefore, identifying wild relatives of wheat and being aware of their diversity, is undeniably effective in expanding the richness of the gene pool and the genetic base of new cultivars and can be a useful tool for breeders in the future. The present study was performed to evaluate the molecular diversity among 49 accessions of the genera Aegilops and Triticum in the National Plant Gene Bank of Iran using two DNA-based markers, i.e., SSR and ISSR. Also, the present study aimed to examine the relationships among the accessions studied belonging to different genetic backgrounds. Results: Ten SSR and tan ISSR primers produced 2065 and 1524 polymorphism bands, respectively. The number of Polymorphic Bands (NPB), the Polymorphism Information Content (PIC), Marker Index (MI), and Resolving Power (Rp) in SSR marker was 162 to 317, 0.830 to 0.919, 1.326 to 3.167, and 3.169 to 5.692, respectively, and in the ISSR marker, it was from 103 to 185, 0.377 to 0.441, 0.660 to 1.151, and 3.169 to 5.693, respectively. This indicates the efficiency of both markers in detecting polymorphism among the accessions studied. The ISSR marker had a higher polymorphism rate, MI, and Rp than the SSR marker. Molecular analysis of variance for both DNA-based markers showed that the genetic variation within the species was more than the genetic diversity between them. The high level of genomic diversity discovered in the Aegilops and Triticum species proved to provide an ideal gene pool for discovering genes useful for wheat breeding. The accessions were classified into eight groups based on SSR and ISSR markers using the UPGMA method of cluster analysis. According to the cluster analysis results, despite similarities between the accessions of a given province, in most cases, the geographical pattern was not in accordance with that observed using the molecular clustering. Based on the coordinate analysis, neighboring groups showed the maximum similarities, and distant ones revealed the maximum genetic distance from each other. The genetic structure analysis successfully separated accessions for their ploidy levels. Conclusions: Both markers provided a comprehensive model of genetic diversity between Iranian accessions of Aegilops and Triticum genera. Primers used in the present study were effective, informative, and genome-specific which could be used in genome explanatory experiments. [ABSTRACT FROM AUTHOR]

The effect of humic acid (HA) on the nutritional value and quality of wheat grain and flour has rarely been studied. A field experiment to determine the effect of fertigation of HA and Azotobacter chroococcum UT1 (AZ) on nutrient elements (P, K, Zn, Fe, Mn and Cu), dry gluten, protein and essential amino acids of two wheat cultivars (Chamran and Kouhdasht), comparing four treatments (control, HA, AZ and HA × AZ) using randomized complete block design with four replicates was conducted at the research station of Chardavol in Iran. The 2 years data during 2019 and 2020 indicated that the fertigation of HA and its interaction with AZ (HA × AZ) improved the nutrient elements, dry gluten and protein concentration of wheat grain and flour in both wheat cultivars in comparison with the control treatment. Inoculation of AZ reduced dry gluten of flour in the Kouhdasht cultivar by 6.34%. The application of HA × AZ also significantly enhanced the concentration of essential amino acids of wheat grain and flour in both wheat cultivars by 7.24–11.6%. In similar treatments, the concentration of nutrient elements, dry gluten, protein and essential amino acids of the Chamran cultivar were higher than those in Kouhdasht cultivar. Therefore, it can be concluded that the combined application of HA and AZ can be a suitable recommendation for improving wheat quality. [ABSTRACT FROM AUTHOR]

To find suitable farming management approaches in the semi-arid climate of Iran, we set up an experiment combining three farm management practices with four crop rotation systems over four growing seasons (two winter and two summer seasons), from 2018 to 2020. The three farm management practices comprised: intensive (IF, with inorganic inputs, removal of crop residues from the soil, and weeds chemically controlled), organic (OF, with organic inputs, a return 30% of crop residues in the soil, and weeds mechanically controlled), and integrated (INT, with mineral/organic inputs, return 15% of crop residues to the soil, integrated weed control). The four crop rotation systems were: fallow-wheat (F-W), maize-wheat (M-W), sesame-wheat (S-W), and mung bean-wheat (B-W). Treatment effects were assessed by chlorophyll (Chl) content, photosynthetic parameters, and wheat grain quality and quantity measurements. All management practices from the first to the second year resulted in increases in the total Chl content and post-anthesis photosynthesis (PAP). The total Chl content under INT was improved through a greater increase in Chl-b compared to Chl-a. Dry matter remobilisation (DMR) was higher under INT than under IF. The highest (39) and lowest (23) grain number per spike were obtained in IF under B-W and OF under F-W, respectively. B-W produced the highest grain yield (541.4 g m−2). The protein contents in farming with organic matter inputs were higher than that under IF. INT produced an optimum level of wheat yield despite a 50% reduction in chemical inputs, and this was achieved through the fast absorption of chemical elements at the beginning of growth, and having access—at the grain filling stage—to elements derived from organic matter decomposition, and through the utilisation of DMR. Our results indicate that implementing B-W and S-W under INT is a promising strategy for this region. However, the results need to be further evaluated by long-term experiments. [ABSTRACT FROM AUTHOR]

AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588 t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05 t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values ​​for the farms were 0.033 and 0.069 t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066 t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the efficacy of different fertilizer management systems such as organic, chemical and integrated fertilizer systems in increasing total quality and quantity yields of canola and wheat in two rotations included canola–soybean–wheat and canola-wheat. In this experiment, the effects of zeolite incorporation with different organic and chemical fertilizers were investigated on the improvement of yield components, crop yield and nutrient uptake of canola and wheat in Iran during 2014–2018. Two crop rotations of canola-soybean-wheat and canola-wheat provided the whole-plot treatments with nine fertilization management (F1: urea, F2: urea + zeolite, F3: composted manure, F4: composted manure + zeolite, F5: urea + composted manure, F6: urea+ composted manure + zeolite, F7: urea+ azocompost, F8: urea+ azocompost + zeolite and F9: Control) providing the sub-plots. The highest yield of canola and wheat were observed under F6 and F8. Embedding soybean in the rotation was not significantly efficacious in increasing canola and wheat yields. Also, soybean did not reduce wheat and canola yield, it could fulfill its own requirements. Elemental composition of seeds in crops was affected by the treatments. Principal component analysis (PCA) perfectly displayed that organic fertilizers, especially composted manure accompanied with the application of 8 ton ha−1 zeolite and urea (F6) had the most predominant effect on yield and yield components in both wheat and canola. Crop rotation in the short-term programs might not produce desirable results, but it is expected to see the efficacy of crop rotation in long-term farming practices. [ABSTRACT FROM AUTHOR]

One of the crucial issues in developing nations is diminishing the yield gaps. Therefore, accurate yield gap estimation has many real-world uses for increasing crop production. Utilizing comparative performance analysis (CPA) techniques, the yield gap of wheat fields was evaluated in this study. In Varamin, Tehran Province, Iran, data on 104 wheat fields were collected between 2018 and 2020 and every aspect of wheat field management has been documented. The CPA model determines the yield gap's contributing factors and potential yield. The results of data analysis revealed that the production ranged from 2600 to 7600 kg ha−1. The CPA method predicted a potential yield of 9316 kg ha−1 and found a yield gap of 3748 kg ha−1; this amount was 40.23% of the potential yield. Leaf chlorophyll (29%), irrigation at stem extension (9%), LAI (7.7%), soil salinity (8.2%), field area (16.3%), phosphorus consumption (6%), nitrogen utilized at the stage of tillering (16%), and HI (7.8%) all contributed to the yield gap in the CPA. It has been said that the computed yield in CPA is a potential yield that can be reached. CPA is a cheap and straightforward tool that could identify yield gaps and their causes in a district without the need for costly experiments. Therefore, developing nations with significant efficiency and yield gaps can use these techniques effectively. [ABSTRACT FROM AUTHOR]

Puccinia triticina is a major wheat pathogen worldwide. Although Iran is within the Fertile Crescent, which is supposed to be the center of origin of both wheat and P. triticina, the knowledge of the genetic variability of local populations of this basidiomycete is limited. We analyzed 12 inter simple sequence repeats (ISSRs) and 18 simple sequence repeats (SSRs) of 175 P. triticina isolates sampled between 2010 and 2017 from wheat and other Poaceae in 14 provinces of Iran. SSRs revealed more polymorphisms than ISSRs, indicating they were more effective in differentiating P. triticina populations. Based on a dissimilarity matrix with a variable mutation rate for SSRs and a Dice coefficient for ISSRs, the isolates were separated into three large groups, each including isolates from diverse geographic origins and hosts. The grouping of SSR genotypes in UPGMA dendrograms was consistent with the grouping inferred from the Bayesian approach. However, isolates with a common origin clustered into separate subgroups within each group. The high proportion of heterozygous alleles suggests that in Iran clonal reproduction prevails over sexual reproduction of the pathogen. A significant correlation was found between SSR and ISSR genotypes and the virulence phenotypes of the isolates, as determined in a previous study. [ABSTRACT FROM AUTHOR]

Climate change is one of the biggest environmental challenges that significantly impact water resources and the quantity and quality of agricultural products. Assessment of these impacts during the historical period and under future climate is essential for achieving a sustainable agricultural system in the face of climate change threats and water scarcity. In this research, we evaluated the yield and water footprint of rainfed and irrigated wheat during the historical period (1986–2015) and two future periods (2016 to 2055) in a semi-arid environment in Fars province, Iran. The future climate data was selected from the CanESM2 model outputs (bias-corrected and downscaled using the SDSM model) under the RCP4.5 scenario, and the yield projection was made using the AquaCrop model. Our result showed that for both irrigated and rainfed wheat, the yield significantly increases in southern parts of the study area in future climates, primarily because of an increase in effective precipitation. Other regions will experience a marginal yield decrease or no yield changes (in the case of irrigated wheat). Our assessments of the water footprint of wheat production showed a significant reduction in green and blue water footprints in the southern regions. In other regions, various patterns emerged for irrigated and rainfed wheat, but an overall increase was observed. The southern regions of the study area will be more suitable for wheat production owing to the higher yield and lower water footprint. [ABSTRACT FROM AUTHOR]

Wheat plays a vital role in the food security of society, and early estimation of its yield will be a great help to macro-decisions. For this purpose, wheat yield and water productivity (WP) by considering soil data, irrigation, fertilizer, climate, and crop characteristics and using a novel hybrid approach called hazelnut tree search algorithm (HTS) and extreme machine learning method (ELM) was examined under the drip (tape) irrigation. A dataset including 125 wheat yield data, irrigation and meteorological data of Mahabad plain located southeast of Lake Urmia, Iran, was used as input parameters for crop year 2020–2021. Eighty percentage of the data were used for training, and the remaining 20% for model testing. Nine different input scenarios were presented to estimate yield and WP. The efficiency of the proposed model was calculated with the statistical indices coefficient of determination (R2), root-mean-square error (RMSE), normalized root-mean-square error, and efficiency criterion. Sensitivity analysis result showed that the parameters of irrigation, rainfall, soil moisture, and crop variety provide better results for modeling. There was good agreement between the practical values (field management data) and the estimated values with the HTS–ELM model. The results also showed that the HTS–ELM method is very efficient in selecting the best input combination with R2 = 0.985 and RMSE = 0.005. In general, intelligent hybrid methods can enable optimal and economical use of water and soil resources. [ABSTRACT FROM AUTHOR]

In rainfed areas, due to variable environmental factors, improving the yield stability of the introduced cultivars along with increasing yield should be considered. The main aim of this study is to obtain high-yield wheat genotypes that are stable and adaptable to cold climatic conditions in Iran. For this purpose, 25 wheat genotypes were evaluated in a randomized complete blocks design with three replications during three cropping seasons (2013-2016) under supplementary irrigation and rainfed conditions. PBSTAT-GE software was used for genotype × environment interaction (GEI) analysis and comprehensive sustainability analysis. The results showed that G5, G14, G16 and G18 genotypes had good stability and general adaptation based on parametric and non-parametric stability statistics. Combined analysis of variance based on the Additive Main Effect and Multiplicative Interaction (AMMI) model showed that GEI is significant in the term of grain yield. Also, the ratios between the sum of squares G, GE and IPC1 showed that the AMMI is suitable for data analysis. GGE biplot analysis identified five mega-environments (MEs), in which ME I including E1, E2, E3, E4, E5, E6, and G7, G5, G14, G13, G16, G18, G20 being the superior ME I genotypes. According to AMMI and GGE biplote stability methods, lines G20, G18, G13, G16, G14 and Saein cultivar (G5) can be considered as desirable genetic resources in wheat production programs under variable environments in Iran, due to having the appropriate combination of yield and stability. [ABSTRACT FROM AUTHOR]

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Fifteen million tons of bread is annually produced in Iran of which the 80% of share in production belongs to traditional bakeries. The present study examines the aspects of the energetic, economic, and environmental impacts of production of Iranian breads (Barbari, Lavash, and Sangak). The results showed that energy demand of a typical baking process of Iranian breads is 22.93–26.08 MJ kg−1. The annual energy utilization of these bakeries is 239.57 TJ, accompanied with 13,308 ton year−1 of CO2 emissions. Sangak bread production had the highest energy efficiency, energy productivity, and the lowest rejected energy. In terms of economic aspects, the highest net income, profit to cost, and economic productivity, were related to Barbari bread production. Sangak bread production had the least environmental impacts. Lavash bread production, although it is the thinnest bread, had the highest energy consumption, was responsible for emitting the largest share of Greenhouse Gas (GHG) to the atmosphere and it had the lowest economic return. The flour was the most influential input on the energy efficiency, cost, and GHG emissions among the three examined types of bread production. Therefore, improving wheat cultivars and flour production process, may reduce the energy consumption and the GHG emissions in the bread industry. [ABSTRACT FROM AUTHOR]

The occurrence of freezing stress around herbicides application is one of the most important factors influencing their performance. This experiment was performed to evaluate the efficacy of clodinafop-propargyl and 2,4-D plus MCPA (2,4-Dichlorophenoxyacetic acid plus 2-methyl-4-chlorophenoxyacetic acid), the most important herbicides used in wheat fields in Iran, under the influence of a freezing treatment (-4°C). Wheat and its two common weeds, winter wild oat (Avena ludoviciana Durieu) and turnipweed [Rapistrum rugosum (L.) All.], were exposed to the freezing treatment for three nights from 7:00 P.M. to 5:00 A.M. before and after herbicide application, and their response was compared with plants that did not grow under freezing stress. Under no freezing (NF) and freezing after spray (FAS) conditions, winter wild oat was completely controlled with the recommended dose of clodinafop-propargyl (64 g ai ha-1; hereafter g ha-1). However, the survival percentage of winter wild oat in the freezing before spray (FBS) of clodinafop-propargyl 64 g ha-1 was 7%, and it was completely controlled with twice the recommended dose (128 g ha-1). Under NF conditions and FAS treatment, turnipweed was completely controlled with twice the recommended dose of 2,4-D plus MCPA (2025 g ae ha-1; hereafter g ha-1), while there was no complete control under recommended rate. However, in the FBS treatment, the survival of turnipweed was 7% under double dose. The LD50 (dose required to control 50% of individuals in the population) and GR50 (dose causing 50% growth reduction of plants) rankings were NF

Wheat streak mosaic tritimovirus (WSMV) seriously damages wheat worldwide. We report analyses of new complete ORF (CO) sequences from seven Australian isolates with 56 COs and 128 coat protein (CP) genes sequenced previously. Eleven CO and three CP sequences were recombinants so were removed from our analyses. Patristic distances of maximum‐likelihood phylogenies of nonrecombinant (n‐rec) CO sequences and their CP sequences were closely correlated (R = 0.994, p ≤ 0.00001). The phylogeny of all 188 n‐rec CP genes had four well‐supported phylogroups (I–IV): phylogroup I (one Mexican sequence), phylogroup II (six Iranian sequences), phylogroup III (48 sequences) and phylogroup IV (133 sequences), each with basal Iranian sequences and either mostly European (phylogroup III) or mostly American (phylogroup IV) terminal sequences. Australian and South American sequences formed a phylogroup IV subcluster within a Pacific Northwest USA cluster. Unlike the Iranian, South American and European populations, the North American and Australian populations demonstrated recent population imbalance. Sample collection dates of 40 CO sequences are known, allowing WSMV phylogeny dating by RTDT methodology. The most recent WSMV ancestor was dated at 1456 CE, and the Australian cluster at 1998.7 CE, only 2–3 years before WSMV was first reported. Tritimoviruses originated in central Eurasia, WSMV first entering wheat in its Middle East domestication centre and one basal lineage being taken to Mexico after the Spanish conquest, whereas the other most basal lineage spread throughout Iran, before spreading to other world regions. Probable future spread to other world regions of additional WSMV phylogroups, and of interphylogroup recombinants, constitutes a biosecurity concern. [ABSTRACT FROM AUTHOR]

Climate change is one of the most important threats to food security. Earth's temperature is reported to increase by 1.5 to 4 °C by the end of the twenty-first century, compared to the base period (1850–1900), and will continue after 2100. Different models have been used to investigate the effects of climate change on different plant responses, including the exponential downscale statistical model of SDSM. Photosynthesis, respiration, and production are some of the first components to be affected by temperature which are discussed here. This study was aimed to introduce and compare the best interpolation method of main temperatures and precipitation to simulate the rate of photosynthesis, total respiration (total growth and maintenance respiration), and dry matter production of wheat in Golestan Province under climate change. Long-term data of 38 synoptic meteorological stations were used to interpolate the main temperature variables and provide reliable maps. Then, temperature change (ΔT) was used to simulate photosynthesis, total respiration, and dry matter production using the canopy photosynthesis simulation model (Can_Phs). The results clearly showed that by changing the minimum temperature by 1.1 to 3.1 °C and the maximum temperature by 2.3 to 4 °C, the amount of wheat production in the study area will be affected in 2050. This increase in temperature can reduce the length of the growing season in autumn wheat and limit the duration of intercepting light and capturing other resources, which in turn leads to a decrease in photosynthesis and increased respiration during the growing season. [ABSTRACT FROM AUTHOR]

Determining soil characteristics and land limitations through the land suitability assessment process is a very important step, due to its important role in sustainable management and reducing the effects of land degradation for the continuous production of agricultural crops. In this study, land suitability potential for wheat cultivation was determined by analytic network process (ANP)–fuzzy and traditional parametric methods including Storie and square root in lands with an area of about 4300 ha located in northwestern Iran. For this purpose, according to the land use maps, topography, slope, geology, and satellite image of the study area, the initial soil units were identified and soil was sampled by digging profiles in these units from the soil horizons of the profiles. According to the results, the ANP-fuzzy method had a more accurate evaluation in determining the land suitability potential, based on the determination coefficient (R2) value between wheat yield and land suitability index compared to parametric methods. The application of ANP-fuzzy method determined 2328.05 ha, 53.79% of land highly suitable (S1), 1577.69 ha, 36.45% moderately suitable (S2), 419.6 ha, 9.69% marginally suitable (S3), and 3.02 ha, 0.07% currently unsuitable (N1). The most important limiting characteristics were slope, soil depth, ESP, Gypsum, pH, and soil salinity. Therefore, due to optimal weighting of characteristics, determining suitable limits of critical points, and applying ideal fuzzy membership functions, the ANP-fuzzy method can be applied as a superior method for assessing land potential for sustainable agricultural crop production in other parts of the world. [ABSTRACT FROM AUTHOR]

Improvement of grain yield is the ultimate goal for wheat breeding under water-limited environments. In the present study, a high-density linkage map was developed by using genotyping-by-sequencing (GBS) of a recombinant inbred line (RIL) population derived from the cross between Iranian landrace #49 and cultivar Yecora Rojo. The population was evaluated in three locations in Iran during two years under irrigated and water deficit conditions for the agronomic traits grain yield (GY), plant height (PH), spike number per square meter (SM), 1000 kernel weight (TKW), grain number per spike (GNS), spike length (SL), biomass (BIO) and harvest index (HI). A linkage map was constructed using 5831 SNPs assigned to 21 chromosomes, spanning 3642.14 cM of the hexaploid wheat genome with an average marker density of 0.62 (markers/cM). In total, 85 QTLs were identified on 19 chromosomes (all except 5D and 6D) explaining 6.06–19.25% of the traits phenotypic variance. We could identify 20 novel QTLs explaining 8.87–19.18% of phenotypic variance on chromosomes 1A, 1B, 1D, 2B, 3A, 3B, 6A, 6B and 7A. For 35 out of 85 mapped QTLs functionally annotated genes were identified which could be related to a potential role in drought stress. [ABSTRACT FROM AUTHOR]

Agricultural systems involve intricate interdependencies among water, energy, and food. Increasing understanding of these linkages, along with implications for greenhouse gas (GHG) emissions and developing new assessment approaches are critical for achieving key sustainability goals. 1) Evaluation of the impacts of tillage, irrigation and residue management practices on water and energy consumption, and GHG emission in the cultivation of irrigated wheat and rapeseed, 2) Developing a novel Water-Energy-Food-Greenhouse gas (WEFG) nexus index to provide a holistic assessment of the linkages among water, energy, food, and GHG emissions in agriculture, and 3) Assessing the sustainability of irrigated wheat and rapeseed cultivation under different methods of tillage, irrigation and residue management applying the WEFG nexus index. This study formulated a new WEFG nexus index applying eight indicators of water and energy consumption, CO 2 -eq (CO 2 equivalent) emission, water and energy mass productivity, water, energy and CO 2 -eq economic productivity to evaluate the sustainability of wheat and rapeseed cultivation under two field management practices: 1) furrow irrigation with conventional tillage (FICT), and 2) center pivot irrigation with no-tillage (CPNT), within a semi-arid region in northeastern Iran. Irrigation in both systems was done by applying a deficit irrigation approach. CPNT resulted on average in 46% and 53% energy consumption reductions from water and diesel usage, respectively, compared to FICT. The mean CO 2 -eq emission under CPNT was 26% lower than that recorded under FICT. Furthermore, the WEFG nexus index score for wheat and rapeseed under CPNT was 0.91 and 0.73, respectively, out of 1, compared to 0.18 and 0.12 for FICT. These scores suggest that the CPNT approach is a more appropriate strategy than FICT, as it effectively reduced water and energy consumption while aligning better with long-term environmental and economic aims. The study applies more accurate GHG emission-based indicators to introduce a new WEFG nexus index, and it uses these for evaluation of different field management at the farm level to reduce the uncertainties in the large-scale studies. The proposed methodology for assessing multiple aspects of the WEFG nexus can change previous perceptions about agricultural management in other regions confronting multiple resources and environmental crises. • A new Water-Energy-Food-Greenhouse Gas (WEFG) nexus index was developed. • WEFG was evaluated under different irrigation and tillage methods. • Diesel and electricity had the largest share in energy consumption. • Center-pivot irrigation without tillage reduced water and diesel consumption. • Furrow irrigation and conventional tillage showed the highest CO 2 emissions. [ABSTRACT FROM AUTHOR]

Fusarium crown rot (FCR), caused by various Fusarium species, is a primary fungal disease in most wheat-growing regions worldwide. A. tauschii, the diploid wild progenitor of the D-genome of common wheat, is a reservoir of genetic diversity for improving bread wheat biotic and abiotic resistance/tolerance. A worldwide collection of 286 A. tauschii accessions was used to evaluate FCR resistance. Population structure analysis revealed that 115 belonged to the A. tauschii ssp. strangulata subspecies, and 171 belonged to the A. tauschii ssp. tauschii subspecies. Five accessions with disease index values lower than 20 showed moderate resistance to FCR. These five originated from Afghanistan, China, Iran, Uzbekistan, and Turkey, all belonging to the tauschii subspecies. Genome-wide association mapping using 6,739 single nucleotide polymorphisms (SNPs) revealed that two SNPs on chromosome 2D and four SNPs on chromosome 7D were significantly associated with FCR resistance. Almost all FCR resistance alleles were presented in accessions from the tauschii subspecies, and only 4, 11, and 19 resistance alleles were presented in accessions from the strangulata subspecies. Combining phenotypic correlation analysis and genome-wide association mapping confirmed that FCR resistance loci were independent of flowering time, heading date, and plant height in this association panel. Six genes encoding disease resistance-related proteins were selected as candidates for further validation. The identified resistant A. tauschii accessions will provide robust resistance gene sources for breeding FCR-resistant cultivars. The associated loci/genes will accelerate and improve FCR in breeding programs by deploying marker-assisted selection. [ABSTRACT FROM AUTHOR]

The indiscriminate use of hazardous chemical fertilizers can be reduced by applying eco-friendly smart farming technologies, such as biofertilizers. The effects of five different types of plant growth-promoting rhizobacteria (PGPR), including Fla-wheat (F), Barvar-2 (B), Nitroxin (N1), Nitrokara (N2), and SWRI, and their integration with chemical fertilizers (50% and/or 100% need-based N, P, and Zn) on the quantitative and qualitative traits of a rainfed wheat cultivar were investigated. Field experiments, in the form of randomized complete block design (RCBD) with four replications, were conducted at the Qamloo Dryland Agricultural Research Station in Kurdistan Province, Iran, in three cropping seasons (2016–2017, 2017–2018, and 2018–2019). All the investigated characteristics of rainfed wheat were significantly affected by the integrated application of PGPR chemical fertilizers. The grain yield of treated plants with F, B, N1, and N2 PGPR plus 50% of need-based chemical fertilizers was increased by 28%, 28%, 37%, and 33%, respectively, compared with the noninoculated control. Compared with the noninoculated control, the grain protein content was increased by 0.54%, 0.88%, and 0.34% through the integrated application of F, N1, and N2 PGPR plus 50% of need-based chemical fertilizers, respectively. A combination of Nitroxin PGPR and 100% of need-based chemical fertilizers was the best treatment to increase the grain yield (56%) and grain protein content (1%) of the Azar-2 rainfed wheat cultivar. The results of this 3-year field study showed that the integrated nutrient management of PGPR-need-based N, P, and Zn chemical fertilizers can be considered a crop management tactic to increase the yield and quality of rainfed wheat and reduce chemical fertilization and subsequent environmental pollution and could be useful in terms of sustainable rainfed crop production. [ABSTRACT FROM AUTHOR]

High spatiotemporal climate variability in the northeast Iran has led to increased production risk for rainfed crops. Hence, to explore potential opportunities for reducing the probable risk, it is necessary to characterize the main rainfed growing regions based on drought patterns. The CSM-CERES-Wheat and -Barley models were applied to simulate yield and to achieve input for estimating water supply demand ratio (WSDR) in 10 reference weather stations (RWSs) for the period 1980–2009. Simulated WSDR was averaged for every 100°Cd to and from flowering for each location (each RWS and each season). The environmental classes and their optimum number for each RWSs and the region were derived by k-means clustering. The soil diversity combined with spatiotemporal variations in rainfall caused rainfed wheat and barley to be exposed to drought stresses, which were very different across locations and among seasons. Nevertheless, four main environmental classes could represent the variability at local and regional scales. The first environmental class corresponded to environments with low drought stress with a frequency of 1.10 and 12.01% for wheat and barley, respectively. For wheat, environmental classes 2, 3, and 4 with a frequency of 22.62, 51.46, and 24.82% begin from 1100, 900, and 700°Cd (degree days) before flowering, respectively, and lasted until maturity. For barley, these three environmental classes with the frequency of 26.50, 32.86, and 28.62%, respectively, corresponded to sever drought stress levels with different degrees that begin from 500°Cd before flowering and was maximized at about 300°Cd after flowering which coincided with the grain filling. The occurrence frequency of environmental classes differed temporally and spatially. Over time from 1980 to 2009, environmental class 4 increased and environmental class 1 decreased for wheat and barley, respectively. In most locations, wheat and barley yield tended to decline from environmental classes 1 to 4. Such characterization of production environment can be used to help breeders focus on genes and traits adapted to target production environments. [ABSTRACT FROM AUTHOR]

Evapotranspiration is one of the main components of water balance and its accurate estimation is of great importance in planning and optimizing water consumption. In this study, therefore, it was tried to calculate the actual evapotranspiration rate of wheat crop in the Pars Abad section of Moghan plain, northwestern Iran, which is one of the main agricultural hubs in Iran. The research tools were Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), and Analytical Land Atmosphere Radiometer Model (ALARM) methods. For this purpose, 12 images of Landsat 7 and 8 satellites were used, all of which were in the product development period between 2016 and 2019, and the results were compared with lysimeter data. The results indicated that the highest actual evapotranspiration rate of wheat crop during the development period was related to 2018.07.01 (7.86 mm/day) in the ALARM method and the lowest rate in the mid-growth period belonged to 2017.01.30 (0.32 mm/day) in the METRIC method. Among the investigated methods, the SEBAL method with an RSME of 0.633 had the lowest error rate and the highest R 2 (0.9307) compared with the lysimeter data, followed by the METRIC and ALARM methods with the lowest error (RMSE = 0.761 and 0.855 mm/day) and the highest correlation (R 2 = 0.9057 and 0.8709), respectively. [ABSTRACT FROM AUTHOR]

Potassium (K) is an important cation that regulates plant metabolism. Therefore the effect of different concentrations of potassium (0, 75, 150 kg ha−1 K 2 SO 4) on photosynthesis efficiency of three winter wheat cultivars (Baran, Homa, Hashtrud) was investigated during the growing seasons of 2017–18 and 2018–19 under cold dryland conditions in Maragheh, Iran. Accumulation of potassium ion (K+) was observed to be increased with an increase in the concentration of K 2 SO 4. With an increase in K+ the Hashtrud cultivar was observed to have more relative water content (RWC), normalized differential vegetation index (NDVI), and stomatal conductance (gs) than other cultivars. This resulted in a higher grain yield for the Hashtrud cultivar. RWC (R2 = 0.97), NDVI (R2 = 0.96), and gs (R2 = 0.92) had a positive relationship with KUE (grain yield/unit of K fertilizer used), especially in dryer years. K deficiency induced hydrogen peroxide (H 2 O 2) and malondialdehyde (MDA) concentration in plants. The application of K increased superoxide dismutases and reduced abscisic acid, to maintain the plants' stomatal conductance. Chlorophyll a fluorescence (ChlF) and the calculation of double normalized relative variable fluorescence reveal detailed information's about the response of wheat plants to K application under dryland conditions. The application of a high concentration of K (150 kg ha−1 K 2 SO 4) on Hashtrud plants had a beneficial effect on the ChlF efficiency at different OJIP phases (KJ and JI). We found the efficiency of ChlF at the ΔW K-I phase with the values of F V /F O and PI ABS improved with the application of 150 kg ha−1 K 2 SO 4 and can be correlated with total yield improvement. These observations indicated that the application of a high concentration of K in stressed conditions for dryland areas could improve photosynthetic efficiency and wheat plant performance. • Application of potassium improve photosynthetic efficiency and wheat plant performance. • Potassium deficiency induce free radicle formation in plants. • Application of Potassium declined the concentration of Abscisic acid in wheat cultivars. [ABSTRACT FROM AUTHOR]

Though increasing food supply in order to meet the rising demand for nutrition is a global social imperative, reducing the dependence on imports of essential food commodities is both an economic and a geo-political imperative for national governments. However, in light of the Sustainable Development Goals, although Zero Hunger (SDG2) and Good Health and Well-Being (SDG3) can be ensured within a country when the inhabitants are well-nourished and staple food items remain affordable to one and all, oftentimes, there are trade-offs in the process, with the environmental dimensions—SDGs 13 (Climate action), 14 (Life below water) and 15 (Life on Land). In this paper, using a combination of Environmental-Life Cycle Assessment (E-LCA) and Data Envelopment Analysis (DEA), the authors have evaluated the eco-efficiency of 169 wheat cultivation systems in the Golestan province in the north of Iran. Benchmarking performance based on the best-performing wheat farms and optimizing (decreasing essentially) the consumption of resources, will enable an average reduction of between 10% and 16% in global warming, acidification, eutrophication, and non-renewable energy usage of the wheat cultivation systems in the case study region. The authors recommend the use of this combination not only for wheat cultivation in other regions of the world, but also for other agricultural systems. [ABSTRACT FROM AUTHOR]

Background: Aegilops tauschii Coss. as a donor of wheat D genome has an important role in wheat breeding programs. Genetic and phylogeographic diversity of 79 Ae. tauschii accessions collected from north and northwest of Iran were analyzed based on retroelement insertional polymorphisms using inter-retrotransposon amplified polymorphism (IRAP) and retrotransposon-microsatellite amplified polymorphism (REMAP) markers. Results: In total, 306 and 151 polymorphic bands were amplified in IRAP and REMAP analyses, respectively. As a result, a high level of polymorphism was observed among the studied accessions as revealed by an average of 25.5 bands per primer/primer combination and mean PIC value of 0.47 in IRAP and an average of 25.16 bands per primer combination and mean PIC value of 0.47 in REMAP. Genetic relationships of the accessions were analyzed using distance- and model-based cluster analyses. Conclusion: The result showed that genetic distance did not seem to be related to geographic distribution, and the accessions could be divided into three groups, which was further supported by principal coordinate analysis. These results on genetic diversity and population structure of Ae. tauschii in Iran should provide important knowledge on genetic resources and their applications in wheat breeding programs. [ABSTRACT FROM AUTHOR]

Oral immunotherapy (OIT) is a novel approach to desensitization and tolerance induction in food allergy patients. This study aimed to design and implement a new wheat OIT protocol, evaluate its efficacy in tolerance induction, and assess specific immunoglobulin-E (IgE) and regulatory T cell changes. From 2015 to 2017, 26 patients with confirmed IgE-mediated hypersensitivity to wheat were treated via oral immunotherapy (OIT). Patients with prior anaphylactic episodes underwent OIT using the rush method. Specific IgE concentrations and the number of regulatory T cells (CD4+ CD25+ FOXP3+ T cells) were measured using Allergy Screen immunoblot assay and flow cytometry, respectively. This study was registered in the Iranian Registry of Clinical Trials (IRCT20181220042066N1). The results revealed success rates of 100% and 93.3% for desensitization and tolerance. Specific IgE was significantly reduced after 12 months of OIT. No significant change in regulatory T cell numbers was observed. In view of the promising findings of this study, the proposed OIT protocol could be viewed as an effective and valuable method to induce tolerance and desensitization in wheat allergic patients. [ABSTRACT FROM AUTHOR]

Knowledge about genotype × environment interaction helps breeders to select the best adaptable and stable genotypes for different regions. The main objective of this research was to select higher yielding bread wheat genotypes, compared to control cultivar, that are adaptable to the climatic conditions of the tropical and subtropical rainfed regions of Iran. Thus, 15 bread wheat genotypes selected from advanced yield comparison experiments, along with the check cultivar of Aftab, were studied in a randomized complete block design with three replications for three cropping seasons (2017- 2020) in four regions (Gachsaran, Gonbad, Khorramabad and Moghan). In order to analyze the genotype × environment interaction the multivariate method of Additive Main Effects and Multiplicative Interaction (AMMI) was used. The Results of combined analysis of variance indicated that 91.49, 1.54 and 5.03 percentage of the total variations are related to environment, genotype and genotype × environment interaction, respectively. Moreover, results showed that the first seven principal components of the AMMI model were significant and explained 97.94% of genotype × environment interactions. The biplot of mean grain yield and the first principle component of genotypes and environments revealed that genotypes G1, G12 and G11 with a higher grain yield than the overall mean and lowest genotype × environment interaction were stable with high grain yield. Among them, genotype 11 with suitable general adaptability can be selected as promising genotype and a candidate for introducing a new cultivar for arid and semiarid rainfed regions. In this research, Khorramabad, with high proportion of genotype × environment interaction was recognized as the ideal environment for differentiation and separation of bread wheat genotypes. The cluster analysis classified the studied environments into three groups. The inclusion of all three years experiments related to Moghan location in one group indicates the high predictability and repeatability of this region. [ABSTRACT FROM AUTHOR]

Climate variability plays a pivotal role in rainfed agriculture, especially within arid regions. Analyzing these fluctuations across diverse climatic conditions establishes a foundation for subsequent investigations. In Iran, the FAO 56 aridity index categorizes the nation into very dry, dry, semidry, and humid climate classifications. This study aimed to explore equations derived from multiple linear regression (MLR) and the disparities between predicted and observed yields of rainfed wheat and barley across Iran's varying climates. Meteorological data, encompassing rainfall (R), mean temperature (T mean), solar radiation (S), and wind speed (U 2), were compiled from 44 synoptic stations spanning 1981–2020. These data constituted inputs for the MLR models employed to simulate rainfed wheat and barley yields. The Global Performance Indicator (GPI), a 5-point statistical criteria index, was utilized to assess MLR model performance. The findings unveiled superior MLR model performance in dry climates (R2=0.84 for wheat and R2=0.9 for barley) compared to humid climates (R2=0.69 for wheat and R2=0.66 for barley), evidenced by lower statistical error criteria values. Moreover, across all climates, the MLR models exhibited more accurate predictions for rainfed wheat yield (GPI=1559.3) in contrast to rainfed barley (GPI=1536). In conclusion, this study sheds light on the notable role of climate in rainfed agriculture, showcasing the efficacy of MLR models in predicting yields across varying climatic contexts. • Strong correlation between rainfall and crop yield underscores water's impact on productivity. • MLR models demonstrate higher accuracy in drier climates compared to humid ones. • Rainfall and wind speed emerge as crucial predictors of crop yields, overestimating across climates. • GPI analysis reveals MLR's excellence in estimating yields in dry climates. • Importance of tailored models for accurate yield predictions, with GPI introduced for comprehensive evaluation. [ABSTRACT FROM AUTHOR]

• Classification of wheat flour varieties by FT-MIR spectroscopy and chemometrics. • FT-MIR spectral data were investigated using unsupervised and supervised models. • LDA, SVM and ANN models showed the best results in wheat flour classification. • FT-MIR spectroscopy is able to identify the fraud in wheat flour varieties. Wheat varieties are grown in many countries throughout the world to produce grain and flour for foods, an outstanding and remarkable foodstuff that fulfills human nutritional needs. Methodologically, there is little known about how to distinguish accurately the type of wheat flour and, in the meantime, to restrain food fraud. Therefore, non-destructive methods are necessary for identification, in turn, the spectroscopy method has the potential to be one of the most efficient non-destructive methods. Given the importance of high-purity wheat flour for consumers and food industries, this research introduces the five most cultivated flour varieties in Iran that were classified by FT-MIR spectroscopy. Different preprocessing techniques and unsupervised and supervised models investigated the spectral data of flour varieties. The best outcome came with the standard normal variate (D2 + SNV) preprocessing method and with all supervised models. The accuracy of Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Artificial Neural Network (ANN) was 100% for the test dataset. In conclusion, it is recommended flour industries use the FT-MIR spectroscopy and chemometrics techniques, the combined form of these techniques helps these industries be aware of the purity of produced flour as possible. [ABSTRACT FROM AUTHOR]