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• 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]

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]

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]

• The study formulates a framework for mapping the Cultivation Potential Index (CPI). • CPI was calculated using the climatic perspective of precipitation and temperature. • Precipitation and temperature were prepared by frequency analysis and remote-sensing. • Shannon-Entropy Catastrophe Scheme calculates CPI by decreasing subjectivity. • CPI delineates high-potential areas for rainfed wheat cultivation. Mapping the Cultivation Potential Index (CPI) for a specific crop is a topical research activity in agro-climate studies, which can lead to environmental and water resources protection and agriculture efficiency increase. This study formulates a framework to delineate the CPI of rainfed wheat from the climatic perspective of precipitation and temperature. Four precipitation layers were prepared using the frequency analysis of annual, autumn, spring, and June precipitation time series; also, three temperature layers comprise autumn, spring, and June temperatures obtained by remote sensing techniques. The Shannon-Entropy Catastrophe Scheme (ECS) was also formulated to combine seven layers of precipitation and temperature besides the land use layer while decreasing the inherent subjectivity. The formulated methodology was implemented in the Maragheh city, northwest of Iran. Some contrasts exist between the spatial distributions of precipitation in different time scales, which necessitates the formulation of ECS as a multi-criteria decision-making technique. ECS identifies the eastern half of the study area as the medium to relatively high CPI, which can be employed as a decision support system for planning and managing agriculture. [ABSTRACT FROM AUTHOR]

Drought is one of the most pressing issues in many regions worldwide. Selecting wheat genotypes adapted to changing climatic conditions is crucial for sustainable agricultural production, economic development, and food security. In this study, 156 bread wheat genotypes, including landraces from Turkey, Iran, and Afghanistan, which have adapted to changing climatic conditions for thousands of years, and high-yielding modern wheat varieties, including advanced breeding lines and cultivars were evaluated. The genotypes were subjected to seasonal drought treatments under rain shelters, including pre-heading drought (T1), post-heading drought (T2), irrigation based on long-term precipitation (T3), and seasonal precipitation and supplemental irrigation (T4). Grain yield and physio-morphological and quality traits of the genotypes under seasonal drought and irrigated conditions were evaluated. Drought treatments reduced yield and some physio-morphological traits and increased quality traits. Pre-heading drought treatments had the greatest effect on yield and physio-morphological traits while post-heading drought treatments had the greatest effect on quality traits. Modern wheat cultivars had higher grain yields compared to landraces, but some landraces had a lower yield loss compared to modern wheats under drought stress. The higher tillering rate of the landraces resulted in greater biomass production, taller plant height, and peduncle length. Landraces had higher protein content in all treatments, whereas modern wheats had higher protein content in T2 treatments due to the selection pressure that has been ongoing for years. In summary, given that different drought scenarios will be encountered in the future, landraces should be effectively used in the breeding programs to develop seasonal drought-tolerant modern or climate-resilient wheat varieties. • Drought negative effects yields and physio-morphological parameters. • Pre-heading drought causes more damage than post-heading drought. • The highest increase in quality parameters is in the post-drought. • Grain yield of modern wheat is higher than landraces wheat. • Landraces have high biomass production, plant height, and peduncle lenght values. [ABSTRACT FROM AUTHOR]

• The impacts of climate change (CO2 emissions, temperature, and precipitation) on different types of wheat yield analyzed. • To assist the adaptation and spatial difference effects of climate change on wheat yield, "De Martonne Aridity Index" and FGLS model were used. • At national level, both wheat varieties are acclimated to climate change's impacts in Iran. • Provincial analysis of climate change's effects revealed a varying ranges of results for both arid and semi-arid regions. Wheat is one of the essential food resources, and its cultivation is one of Iran's most vulnerable activities to climate change. In this context, this study aims to unravel the effects of climate change on both irrigated and rain-fed types of wheat yield in Iran and across different regions to provide valuable insights to researchers, policymakers, and farmers. The study will assist in comprehending the adaption and spatial difference effects of climate change on wheat yield. For this purpose, provinces were classified into two climate types based on the De Martonne Aridity Index. The Feasible generalized least squares model was then employed to data from 28 Iranian provinces over the period 2001 to 2019. The index results show that most provinces are in the arid climate group, while the rest are in the semi-arid group. Our findings for the country indicated that changes in climate factors could have positive and negative effects on wheat yield, with results varying by region. Both wheat varieties are acclimated to climate change's effects, and the coefficients of climate factors from 2001 to 2019 are smaller than those from 2008 to 2019. Conversely, semi-arid provinces' irrigated and rain-fed wheat yields were not adapted to the impact of temperature and participation level changes, while irrigated and rain-fed wheat yields were adapted to carbon emissions. In arid provinces, temperature coefficients for rain-fed wheat yield increased from -1.68 to -2.90, while for irrigated type, coefficients decreased from -0.32 to -0.22. The yield of irrigated wheat has not adapted to changes in carbon emissions and precipitation levels. The study's findings can be useful to policymakers and farmers as they plan and alter crop farming to adapt to the effects of changing climate conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

• Red color level of image background at wheat booting can simulate relative yield. • Images were taken from heterogeneous stands of cultivar mixtures with 6 replicates. • The new index (BRL) was more efficient compared with some other vegetation criteria. • The similarities between patterns of RGY & BRL was acceptable even in the mixtures. • It is suggested that the non-vegetation image segments may provide valuable data. Digital cameras are increasingly utilized as a reliable and readily available remote sensing tool in the emerging technology of high throughput phenotyping and agronomic field assessments. In almost every image analysis of green canopies, image segmentation into the vegetation and non-vegetation parts using thresholding is a fundamental step. Therefore, only the vegetation parts of the high resolution images are considered for estimations and the data recorded in the remained pixels will be ignored. In the present study, an accidental observation is described, in which the red color level of the non-vegetation segments of the images taken at the wheat booting phase could simulate the relative pattern of final grain yield. An image archive of heterogeneous wheat canopies (i.e. cultivar mixtures), captured from experimental plots of a field study conducted at the School of Agriculture, Shiraz University, Iran, was analyzed. It was observed that the red color levels of the image background (BRL, mostly composed of soil pixels) were accurately simulating the final grain yields of the treatments, (i.e. very precise mimicry of relative grain yields –RGY– in monocultures, and exact determination of curve extrema and also the direction of fluctuations, in the case of mixtures). Compared with other colors and image-derived criteria evaluated, BRLs had the pattern most similar to the relative grain yields. Moreover, assessments showed the importance of using higher number of replicates in analyses (i.e. 6 vs 3 replications). This observation suggests the red color level of the image-background as a potential novel index for the prediction of wheat grain yield during the preanthesis period; and also the ability of the non-vegetation image segments to provide valuable data for crop remote sensing and image processing studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • The wheat yield sensitivity to different stages of growth and various variables has been investigated. • Growing degree days (GDD) and crop coefficient (Kc) were introduced as crop-specific variables. • The mid-season stage was identified as the best time window. • By using the proposed approach, wheat yield can be achieved before the harvest season. Cereals, especially wheat, have always been one of the main bases of the global food chain. Food security in the future will depend on more production or production with higher yields. So, it is critical to provide approaches to identify factors affecting wheat yield. Prior research has partially overcome the limitations of previous methods (field surveys, empirical statistical models, and crop models). A combination of climate and satellite inputs has been considered in previous research. But the main missing part is insufficient attention to crop-specific variables. A user-friendly interface is another issue that has been neglected because using satellite images requires time and expert effort. Here, to cover these gaps, the variables affecting the wheat yield in different stages of growth were identified and analyzed. The data of 17 years (2001–2017) from 31 provinces of Iran were used to evaluate the proposed approach. Six climatic variables (maximum and minimum temperature, relative humidity, wind speed, precipitation, and sunshine hours) from 238 weather stations and one variable for geographic yield bias (Y baseline) were considered. Also, growing degree days (GDD) and crop coefficient (K c) were introduced as crop-specific variables. First, the relative contribution of each variable was determined based on grey systems theory (GST) in different growth stages. Finally, the sensitivity analysis of input variables in each growth stage was performed based on eight scenarios and machine learning models, including artificial neural network (ANN), least squares support vector regression (LS-SVR), and adaptive neuro fuzzy inference system (NF). The results revealed that the mid-season stage suitable time window and the K c , maximum temperature, relative humidity, Y baseline , precipitation, and GDD with R2 = 0.958, WI = 0.989, and SI = 0.051 were the most influential variables in wheat yield estimation with ANN model. Estimating wheat yield before the harvest season can confirm the ability of the applied models in real-time applications. [ABSTRACT FROM AUTHOR]

Abstract Facing and controlling the adverse effects of global warming on crop production, especially under arid and semi arid conditions is an important question, which must be addressed by research work. This may be achieved by alternating crop phonological properties including the vernalization stage, essential for the vegetative/reproductive transition of winter wheat (Triticum aestivum L.). Shortening vernalization, during spring, can be a suitable method for the earlier production of crop plants, especially under stress conditions. Accordingly, it was hypothesized that it is possible to shorten vernalization, using priming (seeds) and spraying (plants) treatments while achieving optimum yield, in winter wheat, under the arid and semi arid conditions of Isfahan, Iran. The objective was to investigate the effects of plant growth regulators including gibberellic acid (GA, 100 mg/l), kinetin (100 and 200 mg/l), 6-benzyl adenine (BA6, 50 mg/l) and cold stratification on shortening vernalization in winter wheat. The research was a split plot experiment on the basis of a completely randomized block design, with four replicates, conducted in 2016. Different traits including number of fertile tillers (NFT), number of grains per spike (NGS), weight of 1000 grains (W1000), number of spikelet per spike (NST), grain yield (GY), protein percentage (PRO), and moist gluten percentage (MGP) were determined. The results indicated that priming and spraying winter wheat with the single or combined use of GA, kinetin and BA6 is the most suitable replacement for vernalization and increasing grain yield, under arid and semi arid conditions. However, priming winter wheat with cold stratification was not as effective, compared with the use of plant growth regulators. The highest grain yield (15.13 t/ha) was resulted by autumn planting + B8 (GA + BA6), followed by spring planting + GA and B9 (14.82 t/ha). Spraying wheat planted in spring (without priming and vernalization) with kinetin + GA3+GA7 (100 mg/l), increased PRO and MGP. The important results of this research work are: 1) planting winter wheat as spring wheat (vernalization not required) resulted in optimum yield amounts by priming and spraying techniques using gibberellins, kinetin and 6-benzyl adenine. This is of significance, with respect to the issue of global warming. 2) Shortening vernalization in winter wheat, which is especially important under arid and semi arid conditions, as the plant is subjected to different stresses including drought. 3) Improving wheat grain quality by the increased rate of protein and gluten. It is possible to plant winter wheat under arid and semi arid conditions using gibberellins, kinetin and benzyl adenine. Such a method results in alleviating the adverse effects of global warming on wheat production. It is also possible to plant winter wheat under different stresses including drought and cold by controlling the vernalization process. Graphical abstract Image 1024 Highlights • Using plant growth regulators, gibberellins, kinetin and benzyl adenine. • Shortening vernalization in winter wheat (Triticum aestivum L.) • Winter wheat planted as spring wheat with optimum yield without vernalization. • Enhanced protein and gluten percentage in wheat grains. [ABSTRACT FROM AUTHOR]

Wheat is a strategic crop for Iran with respect to food security and environmental impact. In the northeastern Golestan Province irrigated and rainfed wheat production systems co-exist. In the light of climate change the comparative assessment of the two systems regarding productivity and sustainability is of urgent importance. This paper aims to contribute to a more informed debate on the viability of irrigation in marginal crop production regions. For this purpose, the present study investigates productivity and environmental impact of 540 wheat producing farm households (259 irrigated; 281 rainfed) in Golestan by partial life cycle assessment (LCA) methodology using greenhouse gas (GHG) emissions and product carbon footprint (PCF) as environmental sustainability indicators. The study finds huge heterogeneity among wheat producers with regard to yield, GHG emissions and PCF. Comparing irrigated and rainfed production showed that irrigation in average leads to 22% higher yields. However, the environmental impact of irrigated production is disproportionally high leading to 110% higher GHG emissions and 62% higher PCF, which highlights the trade-off between productivity and environmental sustainability. The major contributors to total GHG emissions of wheat production are energy for irrigation (only in irrigated production), N 2 O emissions related to fertilization and residue handling, diesel for machinery, and emissions related to fertilizer production and transport. Increased GHG emissions and PCF in irrigated compared to rainfed production are not only attributed to the energy required for irrigation, but also to increased fertilizer and other production inputs. Splitting the farmers into high, medium and low yield groups indicates the potential for increasing yields through increased input intensity without negative effects on PCF. The study's results demonstrate the potential of better balancing the trade-offs between productivity and sustainability of wheat production in marginal wheat production regions of the world. [ABSTRACT FROM AUTHOR]

Cropping system models (CSMs) are useful tools for developing and optimizing crop management strategies under specific soil and climate conditions. This requires good quality weather data, which is often lacking in many agricultural areas of the world. Studies exposed the capability of gridded weather data to be a proper source in situations where measured weather data is not available. This study assessed the accuracy of a recently published gridded weather dataset called MSWX for modeling of wheat performance in Iran. MSWX has a 0.1°× 0.1° spatial and 3–hourly temporal resolution and covers 1979 to present, and it was initially generated using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 gridded data. For this study, the CSM-NWheat model within Decision Support System for Agrotechnology Transfer (DSSAT) was applied for modeling of wheat production of three irrigated and three rainfed cultivars at six sites in Iran during 2000–2010 with observed daily weather data. Results showed that MSWX maximum and minimum temperatures (Tmax and Tmin) had higher accuracy than AgERA5, while MSWX solar radiation had weaker performance than AgERA5. The bias of MSWX solar radiation was significantly greater than AgERA5 in some of sites. Comparing the NWheat model outputs based on measured and MSWX temperature (solar radiation) data, grain yield was estimated using MSWX with a MAE of 172 (133) kg/ha and a NRMSE of 11.4% (7.6%). The MSWX temperature (solar radiation) data had high accuracy for simulation of flowering and maturity dates with MAE of 3 and 5 (1 and 3) days and NRMSE of 5.4% and 7% (2.1% and 4.5%). The MSWX temperature data was found as the best replacement for measured temperature data in the study area. Also, it seems that MSWX solar radiation has the potential to be an adequate substitution for measured solar radiation data after further bias correction. • MSWX gridded weather data was evaluated for modeling of wheat performance. • The DSSAT-CSM-NWheat model was used for simulation. • MSWX temperature had high accuracy for the study area. • MSWX solar radiation had strong performance, while needs further bias correction. [ABSTRACT FROM AUTHOR]

Iran is mostly located in arid and semiarid regions which makes agricultural water management considerably important in this country. In this research the concept of ‘water footprint’ (WF) is applied at the regional scale for the first time in the country. The calculation framework of Ababaei and Ramezani Etedali (2014) was adopted and modified to better account for the gray and white WFs. The weighted average of each WF component (green, blue, gray and white) and the national total water footprint (NTWF) of the production of main cereals (wheat, barley and maize) were calculated. The NTWF of wheat, barley and maize production were estimated 36,777, 7975 and 3744 million cubic meters (MCM) per year for the period 2006–2012. The ratio of total green WF of three crops to the aggregated NTWF (i.e. all crops) was 43%, and the ratios of the green WF to the NTWF were 47%, 42% and 2% for wheat, barley and maize, respectively. These results show that wheat and barley production are significantly large consumers of the green water resources (i.e. effective precipitation). This implied that there are great opportunities to improve the green water productivity through increasing yield, especially in wheat and barley rainfed lands. The average national green+blue WFs were estimated 24,628, 5,123 and 1,604 MCM per year for wheat, barley and maize, respectively, and 31,356 MCM per year altogether. The ratios of national gray + white WF to the NTWF were also estimated 33%, 36% and 57% for wheat, barley and maize, respectively. These values show the importance of better irrigation management strategies to reduce the share of gray and white WF which is important in both terms of water resources management and environment conservation. [ABSTRACT FROM AUTHOR]

In order to investigate the effect of different tillage systems (i.e. conventional tillage, reduced tillage and no-tillage systems), on yield performance and yield components of wheat ( Triticum aestivum L.) and chickpea ( Cicer arietinum L.) crops, three experiments were conducted under cold rainfed conditions of Kermanshah province in west of Iran from 2012 to 2014. Results revealed that under reduced tillage, yield of wheat was 5.8 and 11.2% higher than conventional tillage in 2013 and 2014, respectively. However, the yield performance of chickpea and wheat crops in the no-tillage treatment were less than conventional tillage system. Increase in volumetric soil moisture (221, 196 and 258% from 2012 to 2014, respectively) in no-tillage treatment, was observed as compared to conventional tillage. Similarly in reduced tillage treatment, volumetric soil moisture content was 175, 163 and 209% higher than the conventional tillage during 2012–2014. The yield performance of chickpea in the reduced tillage treatment was 9.0, 8.6 12.7% higher than conventional tillage across three years, respectively. Although most of the measured traits of wheat and chickpea improved by reduced tillage, there was not considerable difference between no-tillage and conventional tillage. Findings indicated that yield of wheat and chickpea were significantly responsive to tillage systems and the reduced tillage treatment was the most efficient soil management practices for obtaining more yield as well as conservation soil and water resources for rainfed agriculture in Mediterranean climates. [ABSTRACT FROM AUTHOR]

An accurate crop yield forecast is vital for sustaining food security and preventing famine. Artificial intelligence provides a robust tool for integrating environmental, physiological, and management data and predicting crop yield. In this study, we used a Multilayer Perceptron (MLP) model in its default and hybrid learning modes to predict wheat yield. In its default mode, MLP was trained with the backpropagation algorithm, and in the hybrid mode, MLP was trained with the Water Striders Algorithm (WSA), Sine-Cosine Algorithm (SCA), and Genetic Algorithm (GA). We further considered two ensemble modeling approaches, including the Bayesian Model Averaging (BMA) and Copula-based Bayesian Model Averaging (CBMA). The study area was classified into four homogenous wheat cultivation regions using the Fuzzy clustering approach, and for each region, the models were trained based on 30 years of fertilizer information, harvested yield, and climatic data. Our results showed that hybridizing MLP with an optimization algorithm provides more accurate yield predictions than its default mode. WSA required the lowest computational time, and its combination with MLP resulted in a more accurate yield prediction compared to other optimization algorithms. The accuracy of yield predictions was further improved when ensemble modeling was implemented. Accordingly, the CBMA approach generated the most accurate result with the lowest uncertainty range. The mean absolute error (MAE) of the CBMA was 0.026, 0.058, 0.088, 0.133, and 0.0201 lower than that of the BMA, MLP-WSA, MLP-SCA, MLP-GA, and MLP models. GLUE uncertainty analysis verified the superiority of the hybrid models and ensemble approaches. The predicted yield by CBMA, BMA, and MLP-WSA covered 94 %, 92 %, and 89 % of the observations, respectively. • We used the fuzzy clustering algorithm (FCM) to identify the homogeneous regions of rain-fed wheat cultivation in Iran. • Machine learning approaches were used to predict wheat yield, and the hybrid gamma test was used to select the most optimal inputs. • Modeling was done using Multiple hybrid Multi-Layer Perceptron Models (MLP-WSA, MLP-SCA, and MLP-GA) and ensemble approaches (Bayesian and Copula-based Bayesian model averaging). • Ensemble approaches showed superior performance, and the Copula-based Bayesian generated the most accurate predictions. [ABSTRACT FROM AUTHOR]

In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the two crops are phenologically fairly clearly separated; however, at large scale areas the variance of phenological parameters increases for both crops due to the effects of various climatic and orographic factors which adversely influences discrimination of wheat and barley. Additionally, wheat and barley are usually cultivated as both spring and winter or early and late season crops in some areas, making it more difficult to distinguish them. Therefore, developing a new method based on remote sensing data for effective discrimination of wheat and barley is an important necessity in the field of precision agriculture. To this end, this research presents a new phenology-based method to discriminate barley from wheat. In this study, Sentinel-2 (S2) time-series data of a study site in Iran (Markazi) and two sites in the USA (Idaho and North California), are employed. Spectral reflectance values of wheat and barley are examined during the growing season and a new spectral-temporal feature is successfully developed for automatic identification of the barley heading date. The Relief-f algorithm is then employed to select appropriate spectral features of S2 to distinguish wheat from barley at the heading date. Finally, generated spectral features at the heading date are used as input to Support Vector Machine (SVM) and Random Forest (RF) to produce barley and wheat maps. The Kappa coefficient and overall accuracy (OA) obtained for the three study sites are more than 0.67 and 76%, respectively. The findings of this study demonstrate the potential of remote sensing data to identify the phenological growth stages of barley and distinguish it successfully from wheat. • Spectral profiles of wheat and barley are investigated during the growing season. • Heading date of barley is automatically identified based on a new spectral feature. • New features are developed to discriminate barley during the heading stage. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nine global gridded temperature data (GTD) were assessed for modeling of wheat production system. • The CSM-NWheat model was applied for simulation of wheat growth and development. • AgCFSR, AgMERRA, PGF, and AgERA5 had the strongest performances. • AgERA5 was recommended due to its proper accuracy and temporal coverage from 1979–present. Temperature plays a critical role for plant development and growth, and maximum and minimum temperature are two of the key inputs for the application of crop simulation models. The lack of good observed weather data is a challenging issue in many agricultural regions. Recent studies have used gridded weather data as an alternative to observed weather data for application in agricultural systems modeling. The aim of this study was to identify the most appropriate gridded temperature data (GTD) to be applied in crop simulation modeling. Therefore, nine global GTDs were evaluated for rainfed and irrigated wheat production for six sites across Iran using the Cropping System Model (CSM)-NWheat model. The GTDs included AgCFSR, AgERA5, AgMERRA, CPC, GLDAS, NCEP RII, PGF, POWER, and S14FD. The observed data from the weather stations located in the selected sites were used to evaluate the GTDs. To increase the robustness of the final results, six different rainfed and irrigated wheat cultivars were considered for modeling. Results showed that AgCFSR, AgMERRA, PGF, and AgERA5 had the best performance, while NCEP and GLDAS had the weakest performance. Using AgCFSR and AgERA5, grain yield was simulated with NRMSE ≤ 11.5% and NSE ≥ 0.7. The overall performance of AgCFSR, AgMERRA, and PGF was better that for AgERA5; however, these three GTDs are not recommended for agricultural modeling applications, especially if current weather data are needed. AgCFSR and AgMERRA are only available until 2010, and PGF is available until 2016. Therefore, AgERA5 can be recommended for temperature-related agricultural simulations for the study area in Iran and similar environmental regions, due to its overall performance compared to the observed temperature data and with availability since 1979 to present. Further studies are necessary to assess differences in performance for various environments. [ABSTRACT FROM AUTHOR]

• Six machine learning algorithms were tested to model soil properties. • Urease, phosphatase and organic matter are important variables to predict yield. • Detailed maps of soil properties and wheat yield were produced with limited data. • Developed approach is an effective tool to estimate biological properties and yield. Mapping of soil properties by using novel machine learning (ML) algorithms and optimized environmental covariates is of great importance for agricultural management to enhance crop production. This research aimed at evaluating ML algorithms to predict spatial distribution of soil biological properties and wheat yield in the Southwest of Iran. Topsoil samples (0–30 cm) were collected from a total of 60 sampling locations and wheat grain yield (plot 1 × 1 m) was recorded at each location. Soil properties including urease (Ur), alkaline phosphatase (AP), basal respiration (BR), microbial biomass carbon (MBC), soil organic carbon (SOC), MBC:SOC ratio, and metabolic quotient (q CO 2) were measured. At the first step, Random Forest (RF) model was employed to predict soil biological properties by using terrain attributes, remote sensing indices and soil properties as covariates. In this step, both Variance Inflation Factor (VIF) and Pearson regression were applied to select the most important covariates in predicting soil biological properties and to decrease the dimension of the input space with considering no reduction in prediction accuracy. Secondly, wheat grain yield was modeled using six ML algorithms; they were optimized and evaluated in Caret package with 10-fold cross validation. Results showed the highest prediction accuracy for q CO 2 (R2 adj = 0.80) and the lowest for BR (R2 adj = 0.23). Compared to environmental predictors, soil covariates had a greater effect in modeling Ur, q CO 2 , MBC and MBC:SOC ratio, while, for AP and BR, bands 6 and Chanel Network Base Level were the most important factors, respectively. In prediction of wheat grain yield, both Stochastic Gradient Boosting (SGB) and RF models outperformed with R2 adj of 0.89 and 0.88, respectively. Results indicated that the Ur and AP played the major roles in predicting wheat grain yield and explaining its spatial variability. Our modeling results suggested that soil biological properties and yield can be estimated easily with reasonable accuracy. Overall, their high resolution maps may be useful for decision makers, stakeholders and applicants in agricultural management practices towards precision agriculture. [ABSTRACT FROM AUTHOR]

• Initial & final infiltration rates (IR) in bean field were higher than wheat field. • Cumulative infiltration in the bean field was more than wheat field at all times. • Philip model had the lowest error in estimating cumulative infiltration (CR) & (IR). • Kostiakov-Lewis & Horton models were the most accurate in estimating the (IR). • Kostiakov-Lewis & Philip models were the most accurate in estimating (CR). In this study, Kostiakov, Kostiakov-Lewis, Philip, SCS, Overton, and Horton models were investigated based on the double ring testing in two wheat and bean fields with silty clay texture to determine the best infiltration model in lands of Khuzestan province located in southwestern Iran. The results showed that the average infiltration rates in the wheat field were between 35.47 and 6.35 cm hr−1 at humidity levels of 15.83 and 29.20% before the first irrigation and after the second irrigation, respectively. The average infiltration rates in the bean field with initial water content was 16.43% before the first irrigation and from 13.70 to 61.63 cm hr−1 at a water content of 23.10% after the second irrigation. The lowest and highest root mean square error (RMSE) values (1.21 and 3.14 cm, respectively) belonged to the coefficients of cumulative infiltration models in the wheat field with coefficients of determination (R2) of 0.93 and 0.95, respectively; in the bean field, RMSE values of 1.57 and 3.42 cm with R2 of 0.90 and 0.92 were obtained from Philip and Horton models. The lowest and highest RMSE of infiltration rate components in the wheat field were 0.46 and 0.63 cm min−1 and in the bean field were 0.90 and 1.40 cm min−1, respectively, from Philip and Horton models. The accuracy ranking of models in wheat and bean fields showed that Kostiakov-Lewis and Horton models were the most accurate in estimating the infiltration rate and Kostiakov-Lewis and Philip models were the most accurate in estimating cumulative infiltration. [ABSTRACT FROM AUTHOR]

Precipitation is a very important weather variable for growth and yield of rainfed crops. In many agricultural regions of the world, high-quality precipitation records are not available, and thus, gridded precipitation products (GPPs) have to be applied as an alternative. The main objective of this study was to identify the most accurate GPP for simulating crop yield over a major rainfed wheat production zone in Iran. For this purpose, fifteen global GPPs were evaluated versus the observed precipitation records for the simulation of rainfed wheat growth and development and yield estimation using the Cropping System Model (CSM) CERES-Wheat model embedded in the Decision Support System for Agrotechnology Transfer (DSSAT). The findings showed that multi-source GPPs had generally higher skill for the yield estimation. Considering all statistical and simulation results obtained from three sites during 2000–2010, MSWEP (Multi-Source Weighted-Ensemble Precipitation) was found as the best alternative GPP to the observed precipitation data for rainfed wheat grain yield simulation with normalized root mean square error (NRMSE) of 4.6 and Nash–Sutcliffe efficiency (NSE) of 0.79, while CMORPH (the Climate Prediction Center morphing method) was the weakest with NRMSE of 13.3 and NSE as − 0.81. The results point to differences among GPP, but there is a need to evaluate in other regions if multi-purpose GPPs are in general more reliable than GPPs based on specific sources. • Multiple gridded precipitation products (GPPs) were evaluated for agricultural use. • Most of the selected GPPs have not been ever investigated for crop modeling. • The CERES-Wheat model (embedded in DSSAT) was applied for crop yield estimation. • MSWEP and CMORPH had the most and least accuracy, respectively. • Despite high skill, AgMERRA was not suggested due to its limited temporal coverage. [ABSTRACT FROM AUTHOR]

Abstract: The objectives of this study were to analyze energy use and greenhouse gases (GHG) emissions in various wheat production scenarios in north eastern Iran and to identify measures to reduce energy use and GHG emissions. Three high-input, a low-input, a better crop management and a usual production scenarios were included. All activities and production processes were monitored and recorded. Averages of total energy input and output were 15.58 and 94.4 GJ ha−1, respectively. Average across scenarios, GHG emissions of 1137 kg CO2-eq ha−1 and 291 kg CO2-eq t−1 were estimated. The key factors relating to energy use and GHG emissions were seedbed preparation and sowing and applications of nitrogen fertilizer. The better crop management production scenario required 38% lower nitrogen fertilizer (and 33% lower total fertilizer), consumed 11% less input energy and resulted in 33% more grain yield and output energy compared to the usual production scenario. It also resulted in 20% less GHG emissions per unit field area and 40% less GHG emissions per ton of grain. It was concluded that this scenario was the cleaner production scenario in terms of energy use and GHG emissions. Measures of improvement in energy use and GHG emission were identified. [Copyright &y& Elsevier]

Abstract: Irrigation by highly sodic water has been practiced only in recent years in Iran but has led to impaired productivity of thousands of hectares of agricultural lands. So, the present study was set out to evaluate the effectiveness of different rates and sizes of gypsum as an amendment which improves the physical and chemical properties of soil and crop productivity. A field experiment was conducted in a sodic soil at a farmer''s field in Ramjerd, Fars. The treatments consisted of two gypsum granule sizes (1–10mm and <1mm), and two application methods (adding gypsum to the irrigation water and mixing it with soil) were employed at three soil-applied gypsum levels, i.e., 50%, 75% and 100% of gypsum requirement (GR). The findings of the study showed that the application of gypsum increased soil salinity and hydraulic conductivity but decreased sodium adsorption ratio (SAR) and pH. The increase of wheat yield in coarse gypsum was greater than that observed in fine gypsum but, concerning most soil and plant parameters, the difference between fine and coarse gypsum was not significant at all in terms of rates of GR. Drought condition was found to diminish the effect of gypsum on soil and plants as a 10% reduction in wheat yield was observed in a drought year as compared with a normal year. Plant density, fertile spikelets, and 1000-grain weight were improved as the soil-applied gypsum increased. Regarding chemical composition of leaf, plants grown on un-amended plots had higher Na and lower potassium (K) and calcium (Ca) concentrations in leaf tissue. The application of gypsum caused a significant increase in nitrogen (N), phosphorus (P), Ca, K, iron (Fe), and zinc (Zn) concentrations in wheat tissue but copper (Cu) and manganese (Mn) concentrations remained unchanged. It also increased Ca:Na and K:Na ratios in leaf tissue and had positive effects on crop yield. It was finally recommended that farmers reclaim sodic soil by applying coarse gypsum at the rate of 100% GR since it is a more cost-effective way of reclamation of sodic soil. [Copyright &y& Elsevier]

Abstract: In this paper, adaptive neuro-fuzzy inference system (ANFIS) was used to predict the grain yield of irrigated wheat in Abyek town of Ghazvin province, Iran. Due to large number of inputs (eight inputs) for ANFIS, the input vector was clustered into two groups and two networks were trained. Inputs for ANFIS 1 were diesel fuel, fertilizer and electricity energies and for ANFIS 2 were human labor, machinery, chemicals, water for irrigation and seed energies. The RMSE and R 2 values were found 0.013 and 0.996 for ANFIS 1 and 0.018 and 0.992 for ANFIS 2, respectively. These results showed that ANFIS 1 and ANFIS 2 could well predict the yield. Finally, the predicted values of the two networks were used as inputs to the third ANFIS. The results indicated that the energy inputs in ANFIS 1 have a greater impact on the final yield production than other energy inputs. Also, the RMSE and R 2 values for ANFIS 3 were 0.013 and 0.996, respectively. These results showed that ANFIS 1 and the combined network (ANFIS 3) could both predict the grain yield with good accuracy. [Copyright &y& Elsevier]

Abstract: Groundwater irrigation consumes considerable energy as well as water resources across the globe. Using a case study from Iran, this paper explores how enhanced farm water management can help in reducing groundwater exploitation and subsequently limiting energy consumption and the carbon footprint of the groundwater economy. Groundwater use for irrigated agriculture in Iran has increased vastly over the last three decades. We estimate that groundwater pumping consumes 20.5billionkWh electricity and 2 billion liters of diesel and contributes to 3.6% of the total carbon emission of the country. Thus there is an opportunity to reduce energy use and carbon emissions by pumping less water. However, groundwater use remains important for food security. To identify opportunities for water conservation within agricultural fields, the SWAP model was applied to simulate crop growth and field water balance for three major irrigated crops, i.e. wheat, maize, and sugar beet in the Gamasiab River Basin, one of the highest groundwater using irrigated areas of Iran. The model simulations showed that by adopting improved irrigation schedules and improving farm application efficiencies, water productivity will increase, and irrigation water withdrawals from groundwater can be reduced significantly with no reduction in yields. While these improvements may or may not result in water saving and retarding the ground water decline, depending on the fate of excess application, they will have significant water quality, energy, and carbon implications. Such reduction in irrigation application can result in 40% decline in energy consumption and subsequently carbon emission of groundwater use. [Copyright &y& Elsevier]

Abstract: In most parts of Iran, water scarcity has been intensifying and posing a threat to the sustainability of agricultural production. Wheat is the dominant crop and the largest irrigation water user in Iran; hence, understanding of the crop yield–water relations in wheat across the country is essential for a sustainable production. Based on a previously calibrated hydrologic model, we modeled irrigated and rainfed wheat yield (Y) and consumptive water use (ET) with uncertainty analysis at a subbasin level in Iran. Simulated Y and ET were used to calculate crop water productivity (CWP). The model was then used to analyze the impact of several stated policies to improve the agricultural system in Iran. These included: increasing the quantity of cereal production through more efficient use of land and water resources, improving activities related to soil moisture conservation and retention, and optimizing fertilizer application. Our analysis of the ratio of water use to internal renewable water resources revealed that 23 out of 30 provinces were using more than 40% of their water resources for agriculture. Twelve provinces reached a ratio of 100% and even greater, indicating severe water scarcity and groundwater resource depletion. An analysis of Y-CWP relationship showed that one unit increase in rainfed wheat yield resulted in a lesser additional water requirement than irrigated wheat, leading to a larger improvement in CWP. The inference is that a better water management in rainfed wheat, where yield is currently small, will lead to a larger marginal return in the consumed water. An assessment of improvement in soil available water capacity (AWC) showed that 18 out of 30 provinces are more certain to save water while increasing AWC through proper soil management practices. As wheat self-sufficiency is a desired national objective, we estimated the water requirement of the year 2020 (keeping all factors except population constant) to fulfill the wheat demand. The results showed that 88% of the additional wheat production would need to be produced in the water scarce provinces. Therefore, a strategic planning in the national agricultural production and food trade to ensure sustainable water use is needed. This study lays the basis for a systematic analysis of the potentials for improving regional and national water use efficiency. The methodology used in this research, could be applied to other water scarce countries for policy impact analysis and the adoption of a sustainable agricultural strategy. [Copyright &y& Elsevier]

Abstract: The production of bioethanol from agricultural residues such as wheat, barley, sugar cane, corn and rice in Iran is investigated in this paper. In Iran, agricultural residues are not commonly used for energy application. This paper aims to cover several perspectives on the size of the bioethanol feedstock resource in Iran. Crop residues and sugar cane bagasse are included in feedstock for production of bioethanol. There are approximately 17.86MT of wasted crops in Iran that can potentially produce 4.91GL of bioethanol per year. Wheat, sugar cane bagasse, rice, barely and corn are the most favourable bioethanol production source in Iran. Agricultural waste materials can be used for production of bioethanol fuel. Bioethanol can be considered as the optimum alternative fuel for gasoline. Bioethanol is an environmentally friendly fuel and has the potential to provide comparable engine performance results. [Copyright &y& Elsevier]

Abstract: Wheat (Triticum aestirum L.) is widely grown in Iran as a staple food with high nutritional value; so it is important that sustainable tillage systems are used in its production. Conventional tillage methods rely on excessive use of moldboard plows and disk harrows, and are known to cause several types of damage to soils and land in Iran. On the other hand, conservation tillage methods in recent years have demonstrated positive effects on energy consumption, time inputs, erosion control, soil properties, yields and productivity potential. The success of these methods however, varied with different regional conditions, and therefore these differences must be considered in the selection of tillage systems. The main purpose of this study was to identify and evaluate tillage methods for sustainable wheat crop production in Iran under different climatic and soil conditions. The effects of tillage methods on soil physical properties and crop productivity were studied in various parts of Iran (center, south, west and northeast) with average annual rainfall ranging from 230 to 480mm, and soil texture ranging from silty loam to clay loam. In local projects, tillage systems compared were conventional, minimum and no-tillage, chosen according to site-specific conditions. In a nation-wide project, conventional tillage was compared with reduced tillage. The results indicated that obviously the effects of tillage methods differed over the regions, but in practically all situations, reduced tillage methods (chisel plowing) showed promising results. No significant yield reductions were found when compared to the traditional tillage methods (moldboard plowing). The field capacity using a chisel plow increased from 0.50 to 0.75hah−1. On the other hand, no-tillage methods did not produce favorable results, indicating that some degree of soil disturbance is necessary. Conservation tillage by chisel plowing can be recommended in most provinces of Iran but care must be taken that the sowing operation is done correctly. [Copyright &y& Elsevier]

Abstract: Reducing tillage practices results in lower energy consumption as well as soil erosion protection, structural damage control, and a reduction in time and energy required for seedbed preparation. This research was conducted at the Dryland Agricultural Research Institute in Maragheh region of Iran to determine the amount of input energy for different tillage systems. The study was planned as a completely randomized block design with five tillage systems in four replications. The tillage treatments were: moldboard plow+roller+drill (T1); chisel+roller+drill (T2); cyclo-tiller+roller+drill (T3); sweep+roller+drill (T4); and no-till (T5). Wheat yield parameters and energy indices (net energy gain, energy productivity) were recorded. There were significant differences (1% probability level) among the treatments in terms of energy indices and for wheat yield and biomass values. It was found that T3 had the highest yield and biomass and T1 had the lowest. There were also significant differences (1% probability level) among tillage systems for the energy indices. T1 had the lowest energy productivity and energy ratios and T3 had the highest. The least energy consumed for wheat produced was 8.8MJkg−1 for T5 and the most was 11.8MJkg−1 for T1. Energy consumed for tillage using T1 was 32.5% of the total energy and using T5 was found to be 19% of total energy. It was concluded that T3 was the most efficient overall in the region studied. [Copyright &y& Elsevier]

[Display omitted] • Water stress was dominant around flowering of rainfed wheat (56.8 %). • Half of the rainfed wheat agro-ecosystems are facing with 50 % of severe drought patterns. • Severe drought stress reduced grain yield by1.9 t ha−1. • An extra early-maturity genotype reduced severe drought patterns by 44 %. • Applying a late-maturity genotype is suggested for regions with high rainfall. Drought is considered one of the most common and challenging environmental stresses in the agricultural production worldwide. For improving the rainfed crop yields in the drought-prone environments, understanding the seasonal patterns of drought events in the target environments is crucial. This study applies the APSIM (Agricultural Production Systems sIMulator) crop model to interpret drought stress impact on a check genotype (early-maturity genotype) and mid- and late-maturity genotypes in 69 sites and 16 climate classes with climate data from over 37 years representing the rainfed wheat agro-ecosystems in Iran. Four drought patterns were recognized across the 16 regions. Water stress was dominant during grain filling (41 %) and around flowering (56 %) in studying regions. This stress varied over different genotypes and climate classes. Furthermore, results showed that under current management practices and using the check genotype, about half of the regions in the rainfed wheat agro-ecosystems of Iran (∼1.7 million ha) are frequently subject to severe drought patterns (50 %) resulting in a loss of grain yield of ∼2 t ha−1. Replacing a common genotype with an extra early-maturity genotype, however, reduced by 44 % the occurrence of severe drought patterns which simultaneously increased the occurrence of light-moderate drought stress (i.e. DP1 and DP2) in the warm regions. In general, planting the extra early-maturity genotype had the advantage of producing higher yields in the driest regions. Sowing this genotype reduced the variability in grain yield and the frequency of severe drought patterns in the warm regions. On the contrary, in rainy and cold regions, the best-yielding genotypes were those with a longer growth period. Therefore, in rainy and cold regions with light-moderate stress conditions, the breeders should focus on early-maturity and mid-maturity genotypes. Besides, it is suggested that the late-maturity genotype should be used in rainy regions with a reduced risk of drought stress. [ABSTRACT FROM AUTHOR]

Abstract: Isozyme electrophoresis data of seed extracts from 11 populations of diploid wheat species (Triticum boeoticum Bioss. and Triticum urartu Thumanian ex Gandilyan), distributed mainly in the western and west-northern Iran, were investigated. The five enzyme systems used were peroxidase, polyphenol oxidase, superoxide dismutase, malate dehydrogenase and catalase. The first three were found to be useful as molecular marker for characterization of diploid wheat populations. A total of 13 bands from three enzyme systems were recorded. The value of a ‘Jaccard''s’ similarity coefficient ranges from 0.333 to 1.000. Data analysis was done using clustering method UPGMA. On the basis of Jaccard''s coefficient, the obtained dendrogram supports previous relationship between T. boeoticum and T. urartu as separate species as well as reflecting their distinct gene pools and substantiating their specific recognition despite the overall morphological similarity. [Copyright &y& Elsevier]

Abstract: Seed placement and failure to establish a uniform plant stand are critical problems associated with conservation tillage production of corn (Zea mays) following wheat (Triticum aestivum) in Iran. Our objectives were to evaluate the performance of a conventional corn planter (with runner type openers) equipped with row cleaners and to demonstrate for local farmers an affordable method for reduced tillage corn production. The reduced tillage system studied consisted of disking followed by chisel plowing and another disking ahead of planting. A ground-driven row cleaner was fabricated and mounted to each unit of a common pneumatic single seed planter and used to plant at three forward speeds (4, 7, and 10kmh−1) into no- (NR), half- (HR), and whole- (WR) residue plots. This compares to a typical system of complete removal or burning the previous wheat residue in the region. The amount of residue retained in the row, depth of seed placement, emergence rate and three indices of uniformity in seed spacing were determined. Row cleaners performed best at the highest forward speed, retaining an average residue cover of 920kgha−1 compared to 1350kgha−1 for plots planted without using row cleaners. This 430kgha−1 more residue removal was a significant improvement with regard to soil–seed contact. For the HR and WR plots, residue removal averaged 45% and 70% of that present before planting, suggesting that the row cleaners will work more effectively at high residue conditions. Without row cleaners, initial residue levels resulted in a shallow planting depth, but when row cleaners were used, planting depth was maintained near the target depth (5cm). The desired planting depth was also obtained at 7kmh−1 in the HR plots. Row cleaners significantly increased plant emergence rate with the maximum (3.5plantsd−1 m−1) being observed for seeds planted at 4kmh−1 in the HR plots. This agrees with others who showed high residue reduces plant emergence and grain yield. Planting with row cleaners into HR plots at 7kmh−1 resulted in the lowest miss and precision indices comparable to those of a no-residue plot (16.5% and 17.5%, respectively), and a quality of feed index as high as 69% was obtained at similar conditions. Our results indicate that equipping available planters with row cleaners can result in a successful reduced tillage corn production system and thus satisfy both farmers and authorities promoting conservation tillage. In addition to the environmental benefits of the proposed system, it is expected that the savings in time, fuel and labor would offset the cost of row cleaners. [Copyright &y& Elsevier]

Dryland winter wheat (Triticum aestivum L.) is a major crop in the northwest region of Iran that is generally grown in rotation with 14-month fallow or chickpea (Cicer arietinum L.). Information is limited on the success of more intensive dryland cropping systems with conservation tillage management. An experiment was conducted to determine the effect of five tillage systems on crop yield in a winter wheat–chickpea rotation during a 3-year period on a clay loam (Vertic Calcixerepts) in East Azerbaijan, Iran. The tillage systems consisted of moldboard plowing+disking as conventional tillage; chisel plowing+disking as reduced tillage; sweep plowing as minimum tillage; no tillage without and with previous crop residue.The highest wheat yield in three years was obtained with minimum tillage, whereas the yield of chickpea was greatest under no tillage with or without residue. Winter wheat or spring chickpea yield under no tillage with and without residues was not significantly different. Reduced-tillage wheat and chickpea yields were 14 and 27% higher than with conventional tillage, respectively. Average wheat grain yield with no and minimum tillage was significantly greater (27–31%) than under conventional tillage. No-tillage chickpea yield was significantly greater (24–57%) than under reduced, minimum or conventional tillage. The traditional system of moldboard plowing after wheat and chickpea could be replaced by conservation tillage systems that increase yield and will likely improve soil properties in the long term. [Copyright &y& Elsevier]

Although, the deficit irrigation saves the water consumption, but it may cause the loss of yield. Recently, the nano-fertilizers have been used in the agriculture as an approach to minimize the adverse effects of drought stress. Thus, in this study, a two-year (2016/2017 and 2017/2018 winter wheat seasons) split-plot field experiment was conducted to investigate the effect of the nano-chelating fertilizers on the yield and water use efficiency of the wheat with three replications in Iran. The main plots consisted of full and deficit irrigation (50% of full irrigation requirement), and the subplots included foliar spraying with nano chelated -Silicon, -Boron, and -Zinc fertilizers and control. The total irrigation water was equal to 457 and 228 mm for full and deficit irrigation, respectively for the first year, and it was equal to 378 and 190 mm, respectively for the second year. Deficit irrigation significantly decreased the yield components, relative water content (RWC), and cell membrane stability index (CMSI) leading to reduction of the grain yield by 43%, but it increased the grain protein, WUE, and superoxide dismutase (SOD) and catalase (CAT) activities. Nano-zinc increased the protein percentage of wheat grain in both years and irrigation regimes. The activity of SOD and CAT was improved especially in the deficit -irrigated plants and also the yield, yield components, RWC, and CMSI of wheat by applying the nano-fertilizers under both irrigation conditions. Nano-silica application under deficit irrigation in both years resulted in 28% and 32% increase in the grain yield, respectively, compared to the control. The highest WUE was obtained using the nano-silica under deficit irrigation condition in both experiments. Generally, the results indicated the considerable importance of the nano-silica fertilizer in reducing the damage caused by the deficit irrigation and improving the growth characteristics of the wheat. • Deficit irrigation reduced the grain yield by 43%, but it increased the grain protein and WUE. • Nano-silica fertilizer under deficit irrigation resulted in a 30% increase in the grain yield, compared to the control. • Nano-silica fertilizer is recommended due to compensating the damage of water-deficit stress in wheat production. [ABSTRACT FROM AUTHOR]

• Wild oat tolerance to freezing temperatures was higher than turnip weed and wheat. • Freezing stress had a greater negative effect on turnipweed survival than other species. • F' v /F' m at −16℃ and −20℃ was reduced to zero due to the severity of the damage caused by the freezing stress, 72 h and 24 h after the freezing stress, respectively. • High regeneration capacity after freezing stress in wild oat make it one of the most important weeds in wheat fields. Wild oat (Avena ludoviciana Durieu.) and turnipweed (Rapistrum rugosum (L.) All.) are the most important annual weed species of winter wheat (Triticum aestivum L.) in the Khorasan Razavi province of Iran. This study investigated freezing tolerance in wheat (cv. Mihan) and these two weeds based on electrolyte leakage (EL), survival (SU), regrowth characteristics and maximum photochemical efficiency of photosystem (PS) II (F'v/F'm). Significant differences in EL% at freezing temperatures were noted between these plant species. By decreasing the freezing temperatures, EL% of the leaf in all three plants was increased. The lowest and the highest temperature causing 50 % EL (T EL50) of leaves belonged to wild oat (−16.6℃) and wheat (−15℃), respectively. At a temperature of -12℃, SU% of wheat and turnipweed decreased by 15 % and 20 % compared to the SU% at 4℃, respectively. However, the decrease in SU% in wild oat from 4℃ to −12℃ was not significant. Lethal temperature for 50 % SU (LT 50su) of wheat, wild oat, and turnipweed was −13.7, −14.3, and −13.5℃, respectively, and there was no significant difference between wheat and turnipweed. A general trend observed in our study was that species with higher freezing tolerance scores had a higher regrowth capacity. During the first 24 h after the freezing treatment, the F' v /F' m levels in wheat, wild oat, and turnipweed decreased by 33 %, 33 %, and 28 %, respectively, compared to before the freezing stress. Therefore, the presence of these weeds in wheat fields after freezing stress will be possible. Under optimal conditions and lack of integrated weed management programs, these weeds, especially wild oat, will emerge, set seeds and enrich the soil seed bank and thereby continue to be a problem in the cold climates of Iran, especially in the Khorasan Razavi province. Winter weed management using tillage, non-successive planting of annual winter crops, and crop rotation will reduce the dispersal of these weeds. [ABSTRACT FROM AUTHOR]

• Mixtures of 4 winter wheat cultivars with different ripening patterns were evaluated. • Under water stress, heterogeneity in the ripening pattern and grain yield declined. • No mixture produced higher grain yield than the maximum monoculture. • Cultivar mixtures could not mitigate the post-anthesis water stress significantly. • Despite the general expectations, using cultivar mixtures may have adverse results. Wheat cultivar mixtures have a well-explored potential for improving crop diversity, yield stability, and agronomic features. However, it is unclear whether heterogeneity of phenological traits in such mixtures may help to cope with increasing environmental stresses and uncertainties. To investigate the option of using wheat cultivar mixtures with different ripening patterns for mitigating the adverse effects of post-anthesis water stress, a two-year field experiment was conducted during 2014−15 and 2015−16 growing seasons at the research field of School of Agriculture, Shiraz University, Iran. The factorial experiment was a Randomized Complete Block Design with 3 replicates, in which 15 mixture treatments including monocultures and every 11 possible mixtures of four early- to middle-ripening wheat cultivars were grown under two normal and post-anthesis deficit-irrigation conditions. The results indicated that under the stressful conditions, heterogeneity in the ripening pattern of mixtures was diminished. Consequently, dissimilarities in grain yields as well as various agronomic characteristics of mixture treatments were also lessened. Although cultivar mixtures showed some casual beneficial effects, such advantages were not consistent across all conditions. Besides, cultivar mixtures could not mitigate the post-anthesis water stress significantly. However, only when the total averaged values of Susceptibility Index across the two seasons were assessed, a tendency for decreasing SI (up to 5.9 %) was observed in response to the extended phenology, which may suggest further studies for investigating the cultivar mixtures in long-term cropping systems. Despite the general expectation for beneficial ecological services from cultivar mixtures, in many cases there were disadvantageous blends, which led to a considerable reduction in grain yield and water productivity in single growing seasons. Therefore, it is suggested that unless the performance, and preferably the involved mechanisms of cultivar mixtures are fully understood, use of blends as an alternative for conventional high-input wheat cropping systems may lead to adverse results. [ABSTRACT FROM AUTHOR]

Food system sustainability is an important problem for feeding the current generation such that it does not jeopardize the feeding of the next generations. In this research, we investigate the effective factors of the wheat production system and analyze them using a system dynamics approach. The causal loop diagrams of the relationship between these factors and wheat production are analyzed based on their past behaviors and related research. Then, we simulate the problem based on the flow diagram, and after validating the developed model, we propose several scenarios and analyze and discuss their impact on the different dimensions of wheat production sustainability. Due to the high sensitivity of wheat production to rainfall fluctuations and technology levels, more appropriate planning and investment should be made for the better and more efficient use of these two resources. • Analysis of Factors Affecting Wheat Production as the Main Source of Food System in Iran and Their Relationship. • Wheat production system simulation based on 50-year-old historical data. • Developing different scenarios regarding the technology level, rainfall and soil quality. • Investigation of Sustainability Dimensions of Wheat Production System in Iran. [ABSTRACT FROM AUTHOR]

Today, intensive use of energy sources leads to environmental damages such as global warming and resources depletion. Hence, this study provided energy, environmental and economic overview of wheat cultivation in Hamedan province, Iran. The initial data were collected from 75 wheat farms applying face-to-face interview technique. The prepared data related to the 2017–2018 production cycle. The energy analysis results demonstrated that the total energy consumption and output energy in wheat cultivation were 43054.63 MJ ha−1 and 117407.13 MJ ha−1, respectively. Energy productivity, energy use efficiency and net energy gain were computed as 0.12 kg MJ−1, 2.73 and 74352.50 MJ ha−1, respectively. Economic analysis showed that total value and cost of wheat production were 854.86 $ ha−1 and 366.57 $ ha−1, respectively. Net return was 488.29 $ ha−1 and benefit to cost ratio computed as 2.33 in the investigated region. Wheat environmental impacts were evaluated by applying life cycle assessment methodology. Results of environmental impacts showed the largest emissions were related to marine aquatic ecotoxicity (319757.6377 kg 1,4-DB eq.), abiotic depletion (fossil fuels) (6673.1319 MJ) and global warming potential (624.2944 kg CO 2 eq.). Electricity was a hotspot in abiotic depletion (fossil fuels), global warming potential, freshwater aquatic ecotoxicity, ozone layer depletion and photochemical oxidation impact categories. Cumulative exergy demand results indicated that the rates of non-renewable fossil (7088.05 MJ ha−1) for wheat farms mainly resulted from electricity and nitrogen fertilizer. • Energy, economic and environmental impacts of wheat production was analyzed. • Energy forms for wheat farms were calculated by cumulative exergy demand. • Energy use efficiency of wheat production was calculated to be 2.73. • Significant contributions to GWP resulted from electricity. [ABSTRACT FROM AUTHOR]

• Presenting a new multi-objective model for an integrated wheat supply chain network. • Considering the resiliency to maximize responsiveness and flexibility in the model. • Incorporating sustainability issues in the model and social indicators. • Considering uncertainty in the model by using a novel hybrid approach. • Implementing a real case study in Iran to evaluate the applicability of the model. In addition to the great importance of minimizing total cost, resiliency and social indicators have a special place in the agricultural food supply chain. In this respect, a novel multi-objective model is developed that addresses the design/redesign wheat supply chain network, which comprises several suppliers, existing silos, candidate locations for establishing silos, flour factories, and demand zones in an uncertain environment. To develop the mathematical model, two additional and essential dimensions of the supply chain including social impact and resiliency are simultaneously considered. This model is the integration of several proper indicators of resiliency, social responsibility dimensions through considering three objective functions to minimize the cost, non-resiliency, and negative impacts of social responsibility. Due to the fact that input parameters taint with inherent uncertainty, a hybrid stochastic fuzzy-robust programming is introduced, and four different hybrid approaches are developed. Finally, to demonstrate the superiority of the proposed model as well as solution methodology, a real case study is provided. The obtained results show that simultaneously considering cost, resiliency, and social impacts can significantly assist to improve the performance of the wheat supply chain. Moreover, making trade-off between mentioned measures can help the decision-makers to have the principled preference. [ABSTRACT FROM AUTHOR]

• Water conservation is the best way to increase water management efficiency in agriculture and reduce potential negative environmental impacts. • Residue improves soil tilth and adds organic matter to the soil as it decomposes. • Crop residue can reduce the rate of water evaporation and runoff, increase penetration of water and therefore improve soil water conservation. Wheat (Triticum aestivum L.) residues and water management are major problems in southern part of Iran where irrigated wheat-corn (Zea mays L.)-wheat rotation is a common practice. A 2-year (2015–2016) field experiment was conducted as a split plot design with four replications to determine the influence of different irrigation regimes (50, 70, and 100% of water requirement) and wheat residue rates (0, 25, and 50%) incorporation into the soil on sweet corn (Zea mays L V. Saccharata) growth, grain yield, and some soil properties at School of Agriculture, Shiraz University, Shiraz, Iran. The results showed that the highest plant height (187.12 cm) and leaf area index (3.49) were obtained when the crop was normally irrigated (100% of water requirement) and sown into the 25% incorporated wheat residues. Furthermore, canned yield (68%) and water use efficiency (WUE) increased with normal irrigation and 25% residue incorporation compared to the 50% wheat residue rate. The highest grain protein (14.67%) was achieved under incorporation of either 25 or 50% wheat residue into the soil, while the highest grain sugar (2643.5 mg g−1) was obtained when the crop was moderately irrigated (70% of water requirement) with 25% residue incorporation into the soil. The highest soil nitrogen (N) and soil organic carbon (SOC) were achieved with normal irrigation and 50% residue incorporation in both years.In general, adopting an adequate rate of wheat residue (between 25%–50%) accompanied by decreasing irrigation volume to 70% water requirement of corn is recommended in semi-arid agroecosystems. [ABSTRACT FROM AUTHOR]