2Wheat has an important role in food production of the world and Iran as well. It provides around 40 percent of energy and protein intake of Iran’s people. Closing yield gap can increase wheat production. The first step of closing yield gap is to quantify the yield gap at a given region or country. The objective of this research was to determine wheat (Triticum spp. L. ) yields and production during the two cropping years (2013-2015) in the Parsabad Moghan in Ardabil province, northwest of Iran. To calculate the actual yield, a 22-year of official data and 460 farms under the management of farmers during 2014-2016 were used. The average and maximum attainable yield was estimated using field experiment results (FLD), comparative performance analysis (CPA) and the 90th percentile of record yield distribution. Based on the results of this research, a significant management yield gap was observed between attainable and actual yield (22%). In addition,genetic (i. e., gaps due to genetic selection) and total gaps in wheat were 11% and 36%, respectively. The increase in wheat production with the genetic selection and optimal crop management for the studied 28154 ha was estimated to be approximately 59000 tons with an economic value of 2. 59 million dollars. Based on the results of this research, the yield potential of wheat genotypes can be achieved by closing yield gaps in the study area.

In order to investigate the possibility of quinoa producing in Garmsar, Iran, a factorial experiment conducted in randomized complete block design with three replications in 2018-2019 growing season at Garmsar Agricultural Research Station. The factors were planting date at three levels (March 6th, April 1st and April 6th) and the three genotypes of quinoa (Q26, Q29 and Titicaca). Results showed that the effect of planting date was significant for all studied traits except the harvest index. Also, all studied traits were significantly different in all genotypes. Titicaca planted on March 6th had the highest yield (2276 kg.ha-1).The grain yield and yield components decreased with the delaying the planting date. Compared to early plantings, Latest date, April 6th, led to reduction of all traits, especially grain yield (about 50%). The results of simple phenotypic correlation between the studied traits showed that grain yield per hectare had the highest correlation with plant yield (0.877) and then with leaf area index (0.832), panicle weight (0.815) and number of branches per plant (0.745) that was significant at the 1% probability level.

It is necessary to understand the performance potential and the gap between the actual yields of crops with the achievable function so as to diagnose the limiting factors of performance. The present study has been conducted based on CPA Method in 50 fields of Zavkooh villi (Kalaleh, Golestan, Iran) in 2016 and 2017. The study has measured and recorded all information related to management practices, soil characteristics, and farmer socio-economic status. Furthermore, using stepwise regression, the Comparative Performance Analysis (CPA) Method considers the relation between the variables and yield, with the yield gap rate as well as its causes being estimated to reveal each factor’ s contribution to the creation of the yield gap. Results show that there is a 1818 kg per hectare vacuum between the average real yield (2308 kg ha-1) and the yieldable one (4123 kg ha-1), . Accordingly, the most important factors of yield gap in the region include field size (21. 4%), summer plowing (15%), the time of land preparation and plowing (14. 7%), nitrogen fertilizer (14. 6%), potassium fertilizer (14%), weed density per unit area (10. 8%), seed treatment (8. 1%), and Sub-soiler (1. 4%).

One of the main problems in crop production in Iran is the difference between actual yield and attainable yield which is called yield gap. Thus, identifying yield constraints and yield gap are important. Therefore, a study was conducted in 60 farms in Parsabad Moghan, during 2013-14 and 2014-15 crop years using Comparative Performance Analysis (CPA). In this study, several attributes related to management operations and crop characteristics were recorded and measured. Then, the relationship between different variables and grain yield were considered, using stepwise regression. The results showed that there is a 2317. 42 kg/ha difference (gap) between actual yield (average farmers' yield) and attainable yield (maximum farmers' yield). It was identified that planting time, spike density, the number of vents of split application of urea, the number of land preparation operations, farming experience and repeated use of fungicides with contributions of about 18, 29. 7, 20, 4. 6, 14. 3, and 13. 4 percent, respectively, were responsible for this yield gap. Further analysis of the data revealed that most of the variables listed above can be controlled by the agronomic management. It was concluded that with proper farm management and considering the listed yield gap factors, it would be possible to obtain higher grain yields of about 2316 kg/ha in the irrigated wheat farms, compared to the current average farmers’ yield.

The documentation process is very important for identifying yield constraint factors and yield gap. For this purpose, all managing practices were recorded by monitoring paddy rice fields in Sari region, Iran from 2015 to 2016. Field identifications were undertaken in such a way that they included all the main production procedures with variations in management viewpoints. Results revealed that seed consumption varied from 40 to 95 kg ha-1 and the range of seedling age varied from 20 to 50 days. Planting density was 10 to 66 plants per m2. Nitrogen application by 30% of the farmers ranged from 46 to 83 kg ha-1, and 40% of the farmers applied 83 to 138 kg of nitrogen per hectare. In 73% of the fields, nitrogen was not used after the flowering stage. The range of yield varied from 3100 to 5430 kg ha-1, and in 60% of the studied fields, the paddy yield varied from 4205 to 5200 kg ha-1. In the comparative performance analysis (CPA) model, the actual yield and the yield potential were estimated to be 4495 and 6337 kg ha-1, respectively, and the yield gap was 1841 kg ha-1. Among the five variables entered in the model, the effects of potassium application and biological fight were remarkable so that the paddy yield increase by these variables was 709 and 806 kg ha-1, respectively, that was equal to 39% and 44% of the total yield variation. Therefore, since the calculated potential yield was achieved through actual data in each paddy field, it seems this yield potential is attainable.

Determining the yield limiting factors as well as the optimal level of the limiting factor to reduce the gap between actual and attainable yield plays an important role in increasing the yield of crops. This study was conducted using the Comparative Performance Analysis (CPA) method with the aim of determining the limiting factors of sugar beet yield components (including root yield and sugar content) and also their relative contribution in 2020-2021. This study was carried out by completing the questionnaire in 220 Spring sugar beet fields in the whole country. All data collected were subjected to stepwise regression using CPA separately for the two traits of root yield and sugar content. Based on the models derived from this study, the root yield and sugar content gap was estimated at 81.98 t/ha (57.2%) and 4.88 units (21.7%), respectively. Meanwhile, two physiographic factors including elevation above sea level and geographical longitude accounted for 14.1 and 30.1% of the gap in root yield and sugar content, respectively and a social factor of the farmer’s education level accounted for about 11.9% of the gap in sugar content. Among the factors related to agronomic management that affect root yield gap, 85.8% re related to factors such as the seeding rate, final plant density, irrigation method, sowing date, plant distribution, land leveling, root retention at the field, drought stress, and base nitrogen content with a share equal to 24.7, 11.3, 10.3, 9.8, 8.8, 5.2, 4.1, and 1.9% of the total root yield gap (81.98 t/ha) and 14.2% was related to other factors. Regarding the sugar content gap, the management factors including irrigation frequency, cutting off irrigation date, the amount of organic carbon in the soil, and the disease damage accounted for 24.0, 19.0, 10.3, and 4.6% of the gap (4.88 units), respectively.

One of the main problems of soybean production in the east region of Golestan, Iran is considerabledifference between actual yield and attainable yield which is called yield gap. In recent years, this gap hasbeen impressive, and therefore, identifying soybean yield-limiting factors is essentially needed. Thepresent study was conducted based on CPA method in 73 fields of Kalaleh (Golestan, Iran) in 2016. Inthis study all information related about management practices, soil characteristics and farmer socioeconomicstatus were measured and recorded. Then, using stepwise regression, the relationship betweenvariables and yield was considered. The results showed that there is a 2606. 4 kg/ha difference (gap)between actual yield (average farmers' yield) and attainable yield (maximum farmers' yield). It wasidentified that farming experience, burying plant residues in the soil, planting date, Sprinkler irrigation, the amount of used water and plant density were contributed to yield gap, 30. 1, 5. 1, 14. 7, 13. 8, 13. 8 and22. 5%, respectively. It could be concluded that improving these factors will increase the yield up to1802. 9 kg/ha.

Introduction Occurrence of drought and reduction of rainfall in the future will limit the cultivation of irrigated crops. Thus, it is probable that a part of the present irrigated lands and orchards of Iran may be unavailable for the cultivation of irrigated crops, but it is possible to cultivate rainfed crops in these lands. However, the available potential for cultivation of rainfed crops with respect to the soil type, climate and other factors is not known. Limited water resources, on the one hand, and the growing population along with increasing the need to produce food, on the other, make it necessary to have a comprehensive, practical and accurate program. Therefore, research on this issue is essential. In this study, production potential of rainfed wheat (Triticum aestivum), barley  (Hordeum vulgare L.), chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik) and canola (Brassica napus)  in irrigated lands (fields and orchards) was modeled.Materials and Methods  Weather stations (position and distribution), long-term weather data (15 to 30 years), HC27 soil map, crop management data plant parameters were used to determine the yield in this study using SSM-iCrop2 model. In each zone, the yield was determined and compared with the actual data. In other words, the model output were compared with the actual current rainfed yields of each province and then it was determined that whether the model precision was sufficient for this study. Other calculations (determining the average yield of provinces) and generation of maps were done using ArcGIS V.10.2. The yield obtained by farmers in these lands was considered as 50 and 70 percent of yield potential. Also, the yields were categorized into four classes of excellent, good, medium and non-suitable. This classification is based on economic- agronomic profit of crop harvest.Results and Discussion The results of this study showed that the conditions of rainfed production in each province of the country is suitable/appropriate for some crops and unsuitable/inappropriate for some other. In case the yield of farmers reached 70 percent of yield potential by optimum management, all provinces will be classified into the upper average group (3, 18 and 10 provinces in excellent, good and medium groups) for wheat. For barley 30 (3, 10 and 17 provinces in excellent, good and medium groups), for chickpea 30 (3, 6 and 21 provinces in excellent, good and medium groups), for lentil 31 (13 and 18 and 10 provinces in good and medium groups) and for canola 30 (4, 5 and 21 provinces in excellent, good and medium groups) provinces will be placed in the upper average group. Based on 70 percent of yield potential of canola, barley and chickpea, only on province is placed in non-suitable group. On the other hand, in case the yield of farmers reaches 50 percent of yield potential due to improper management, for wheat 30 (2, 2 and 26 provinces in excellent, good and medium groups), for barley 28 (4 and 24 provinces in good and medium groups), for chickpea 18 (4 and 14 provinces in good and medium groups), for lentil 28 (3 and 10 provinces in good and medium groups) and for canola 25 (5 and 20 provinces in good and medium groups) will be classified into the upper average group. Based on 50 percent of yield potential of rainfed wheat, barley, canola, chickpea and lentil, 1, 3, 6, 13 and 3 provinces were placed in non-suitable group, respectively.Conclusion According to the results of this study, a major part of the provinces will be placed in medium and non-suitable groups in case of improper management, and agricultural productions will not satisfy the needs of the country. Therefore, it is necessary to pay a special attention to agronomic management of rainfed crops, as the agricultural production of the country will not be acceptable unless 70 percent of yield potential is achieved.

Introduction1 Rice (Oryza sativa L. ) is the staple food of more than half of the world’ s population and has an obvious effect in feeding, income and job creation of people in the world especially, Iran. The rice cultivation area in the world during the past years has been from 145 million hectares to over 160 million hectares. The last global statistics showed that paddy yield and white rice production were 742 and 492. 2 million tons respectively in 2014. The same amount is predicted for 2016. yield gap analysis is providing a little estimation of increased production capacity which is one important component in designing food providing strategy in regional, national scale and world-wide surface. Due to the existing anxiety about discussions of food security, studies are also increasing globally and in Iran is necessary to estimate the quantity of yield gap and the reasons behind it by appropriate statistical methods, or in other words, detecting the restricting parameters of potential yield. As it was mentioned several factors prevent farmers to reach attainable yield in many crops. It seemed that by defining the effectiveness of each management parameters on the amount of presented yield gap and consequently farmer’ s knowledge on that matter, the distance between actual yield and attainable yield can be reduced. In this research estimation of potential yield, yield gap and determining yield restricting factors and each of their portions in creating yield gap is investigated. Material and Methods The research was done in 100 paddy fields between the Alborz Mountains range and the Caspian Sea in 2016. In this research, all managerial operations from nursery preparation to harvest for modified rice cultivars were recorded through field studies in Neka, Mazandaran, Iran from 2015-2016. All farm cases are pertaining to improved cultivars. The improved rice cultivars were Shiroodi, Neda, Fajr, Ghaem, Khazar, and Nemat, respectively. Field identifications were done in a way that includes all main production procedure in a specific region with variation in management viewpoint. For defining the yield model (production model), the relationship between all measured variables and the final model was designed by controlled trial and error method. The final model was obtained through the controlled trial and error method, which can quantify the effect of yield limitations. The average paddy yield was calculated by the model by placing the observed average variables (Xs) in the fields under study in the yield model. Thereafter, by putting the best-observed value of the variables in the yield model, the maximum obtainable yield was calculated. The difference between these two has been considered as yield gap. Different procedures of the software SAS version 9. 1 were used for analysis. Results and Discussion Data analysis revealed that seed consumption was varied from 30 to 120 kg. ha-1. The range of seedling age variable was from 20 to 60 days old. In 100 paddy fields planting density were 16 to 40 plants per m2. Nitrogen usage by 26% of farmers was among 69 to 92 kg. ha-1 and 16% of the farmers consumed 92 to 115 kg N per hectare. Potassium application was varied from 0 to 100 kg K ha-1 which within 60% of the field’ s potassium usage was less than 35 kg K ha-1. The range of paddy yield in 100 paddy fields was varied from 6100 to 8200 kg. ha-1 that in 40% of the studied fields, the paddy yield was from 7000 to 7600 kg. ha-1. In the CPA model, the paddy yield increasing related to the effect of N top dressing, K usage and N usage after flowering was 327, 674 and 324 kg. ha-1. Conclusion Therefore, the actual yield and yield potential were estimated to be 7194 and 9241 kg. ha-1, respectively and the yield gap was 2047 kg. ha-1. Therefore, regarding the fact that calculated potential yield was reached through actual data in each paddy field, it has been stated that this yield potential is attainable.