Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the Actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, Actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

Background and Objectives: Food security is a main subject in the world. Many international and governmental organizations to ensure the nutritional needs of human are being explored. According to the restrictions on agricultural lands, an increase in production per area unit is a way to improve food security. Despite high yielding varieties, these varieties do not reach their potential yield due to lack of access to favorable environmental conditions. Therefore, yield gap will be created between potential yield and Actual yield. Before any progress in the agronomic operations of crop, it is necessary to identify the crop’ s potential yield and to calculate the gap between potential and Actual yield. Therefore, the aim of this study was to evaluate the historical trend in different yield gaps and to calculate different mean yield gaps in the studied region. Materials and Methods: Potential and conventional yields were simulated using CropSyst model over 1981-2008 period and researchers’ potential yields over 1994-2008 period, maximum achievable yield over 1989-2008 period and Actual yield over 1981-2008 period were collected from Agricultural Research Center, and Agriculture Organization. In this study, four types of yield gap were calculated as the difference between simulated potential yield and Actual yield (yield gap 1), the difference between researchers’ potential yield and Actual yield (yield gap 2), the difference between maximum achievable yield and Actual yield (yield gap 3) and the difference between the simulated conventional yield and Actual yield (yield gap 4). Results: The trend of changes in Actual yield compared with both simulated potential and conventional yields over 1981-2008 period showed the gap between simulated potential and conventional yields with Actual yield were decreasing. The decrease in the yield gaps was due to decrease in the trend of changes in simulated potential and conventional yields and increase in the trend of changes in Actual yield over the studied period. According to the results, to increase the simulated potential yield, the varieties should be selected which have high radiation use efficiency. While for reducing the yield gap between simulated conventional yield and Actual yield, the varieties with high radiation use efficiency should be cultivated under unlimited water and nitrogen condition. The trend of changes in yield gap between researchers’ potential yield and the Actual yield was fixed yields over the period of 1994-2008 which was due to unchanged trend of both yields over this period. The trend of changes of the gap between Actual yield and maximum achievable yield was increasing over 1989-2008. This was indicated the better management operations of prior farmers to achieve maximum yield, while other farmers did not work hardly to enhance the yield over the studied years. The simulated mean potential and conventional yield, the researchers’ potential yield, the maximum achievable yield and the Actual yield were 6. 35, 6. 6, 6. 0, 4. 42 and 3. 4 t ha-1, respectively, over 1994-2008 period. The differences between these yield levels with the Actual yield were about 3. 0, 1. 0, 3. 2 and 2. 6 t ha-1. Conclusion: It can be concluded that the difference between the management operations performed for achieving Actual yield and the other yield levels can be created a yield gap and the more management differences, the higher yield gap.

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.

The aim of this study is to estimate the linkages between the Actual guaranteed price of wheat and its yield in Tehran province during 2000-2018 by considering the dummy variables of development programs of the Islamic Republic of Iran. This study used the Augmented Dickey-Fuller stationary test, the Johansen Cointegration test, and the Vector Error Correction Model (VECM) to achieve the aim. The results show that the Actual guaranteed price of wheat and its yield are of grade I (1), and based on the Johansen test, the long-run relationship between them is confirmed. The results of the VECM model show that the Actual guaranteed price of wheat has a positive and significant effect on wheat yield in Tehran province. By a one percent increase in the Actual guaranteed price of wheat, the yield will increase 1.07 percent. Also, the coefficient of vector error correction indicates that if in the short-run occurs a sudden shock to the Actual guaranteed price of wheat, it will take about 2 periods to adjust the effect of this shock. Considering that the yield is elastic to the Actual price of wheat, suggesting to estimate the guaranteed price of wheat, based on Actual price (deflated) in the purchasing policy, can encourage the improvement of wheat yield in the country's agricultural lands.

Introduction: Due to limited water resources, increasing water productivity is very important to achieve water security and food security. One of the basic measures in this area is to determine the difference in product performance between the current situation and the potential situation. Methods: Crop potential yield is one of the parameters that can be used to calculate the yield gap and manage water and soil resources based on the factors affecting production. In this study, the potential yield of major crops in the composition of irrigation network in Qazvin plain has been determined by Agricultural Ecology method. Findings: The results showed that the yield gap in the crop composition of major crops in Qazvin plain was 3339 (kg / ha). Also, the average yield gap due to the composition of cultivation, yield gap of each crop and the area under cultivation of crops in the irrigation network of Qazvin plain, was 26. 23%. The results of estimating the potential yield at the level of the irrigation network of Qazvin plain show that not all crops have reached the achievable yield level (75 to 85% of the potential yield) and there has been high yield in crops such as sugar beet, tomato and alfalfa. Also, the research findings showed that the yield gap of strategic wheat crop in the irrigation network of Qazvin plain was 1502 kg / ha. One of the most important achievements in calculating potential performance is to obtain performance differences in different products in the region. Using such data, agricultural management of different crops in an area can be evaluated and finally the priority of cultivation of each crop can be explained. Quantification of production capacity per hectare of farms is needed for decision-making, research, development and investment, and to assist local farmers in farming decisions. In this regard, yield vacuum analysis provides a small estimate of the potential increase in production capacity for a given area.

yield gap analysis provides an essential framework to prioritize research and policy efforts to reduce yield constraints. To identify options for increasing chickpea yield, the SSM-chickpea model was parameterized and evaluated. The model was applied to analyze seed yield at both potential and water limited production levels and subsequently to find the yield gap for nine different locations. Study locations are selected to represent major chickpea-growing areas of Khorasan Razavi province (located between 37o N and 33oS latitude and 61o E and 56 o W longitude). The average simulated potential yield of chickpea across all study locations was 2251 kg ha-1, while for the water limited yield was 1026 kg ha -1, indicating 54% lower due to adverse soil moisture conditions. Average irrigated and rainfed Actual yields were 64% and 79% less than simulated potential and water limited yields respectively. Maximum and minimum of simulated potential yield minus simulated water limited yield (YGMM) and simulated potential yield minus irrigated Actual yield (YGMI) were observed in Torbat-Jam and Quchan, respectively. Generally, YGMI and YGMM showed an increasing trend from the north (including Neishabur, Mashhad, Quchan and Daregaz) to the south of the study province (Torbat-Jam and Gonabad). In comparison to other indices, simulated water limited yield minus rainfed Actual yield (YGMR) was very low because both simulated water limited and average rainfed Actual yields were low in these locations. Overall, YGMR was almost unaffected by the quantity of rainfall received at these locations.

Introduction: Chickpea is the most important legume in West Asia and North Africa especially under rainfed conditions (Silim et al., 1993). Chickpea yield is at low levels in major producing countries (Millan et al., 2006), indicating a need to increase crop yield via crop genetic improvement and enhanced crop management. Genetic and management constraints can be analyzed by using crop simulation models. Crop models are very useful tools to evaluate the potential yield and environment constraints, genetics and management factors (Lobell et al., 2009).The yield gap (Yg) is the difference between Yp (irrigated crops), or Yw (rainfed crops) and Actual yields (Ya).Any improvement of crop management practices requires that the potential yield and its difference with Actual yield be determined and ultimately evaluate the determinants of yield gap (Lobell et al., 2009). Assessment of potential yield and yield gaps can help in identifying the yield limiting factors and it helps us develop suitable strategies to improve the productivity of any crop (Naab et al., 2004). In this study, yield potential and yield gap across the major chickpea-growing regions of the Khorasan Razavi province in Iran were quantified by using the SSM-chickpea model and Actual yield and its variability within farmers’ fields were evaluated. This study tries to determine the potential yield capacity and chickpea yield gap.Materials and Methods: For model parameterization, a field experiment was conducted in a randomized complete design with 4 replications in the research field of the Ferdowsi University of Mashhad. The Chickpea cultivar ILC482 was used in this experiment.The Chickpea model of Soltani and Sinclair (2011) was used in this study. The simulations started from the sowing date and ended at maturity. Finally, the simulated results of LAI, aboveground biomass and grain yield were examined by the root mean square error (RMSE). RMSE was calculated as shown in Eq.1 (Wallach & Goffinet, 1987): “formula”Where Oi is the observed data, Pi is the simulated data and n is the total number of observations.The study was performed at nine regions in the Khorasan Razavi province located in the Northeast of Iran, under two water conditions, i.e. potential and water limited. Irrigated and rainfed Actual yields were based on statistical data at regional level for the period of 2002–2012, which were collected from the Agricultural Jihad of the Khorasan Razavi province (Anonymous, 2012). These yields were averaged out for calculating the Actual yield for each region for which simulations were carried out.yield gapsyield gaps were defined as:YGMM=Simulated potential yield - simulated water limited yieldYGMI=Simulated potential yield - irrigated Actual yieldYGMR=Simulated water limited yield - rainfed Actual yield. Results and Discussion: The results suggest that the Khorasan Razavi province with low Actual Chickpea yields has a high probability of large yield gaps and large potentials to increase current yields. The model simulations showed that the average potential yield of Chickpea for the regions was 2251 kg ha-1, while the water limited yield was 1026 kg ha-1 indicating a 54% reduction in yield due to adverse soil moisture conditions. The average irrigated and rainfed Actual yield were also 64% and 79% less than the simulated potential and water limited yields, respectively.Across all study locations, the potential yields were less variable than water limited and Actual yields, and were correlated with solar radiation during the season (R2=0.63, P<0.05). Generally, YGMI and YGMM showed an increasing trend from the North (including Neishabur, Mashhad, Quchan and Daregaz regions) to the South of this province (Torbat- Jam and Gonabad). In comparison with other yield gaps, the quantity of YGMR was very low because both limited simulated water and average rainfed Actual yields were low in these regions.Furthermore, YGMR was more or less unaffected by the amount of rainfall received in these regions.

To investigate and determine the limiting factors of wheat grain yield and the contribution of each of these factors in creating wheat grain yield gap, this study was conducted based on the CPA method (comparison performance analysis) in the eastern part of Kerman. Fifteen farms in each at Bam, Narmashir and Fahraj cities, (5 farms in each city) studied for two years (2017-2018 and 2018-2019). In this study, all information related to management, climate and agricultural traits (including 45 variables) were measured and recorded. Using multiple regression method, the relationship between wheat yield and all variables were examined step by step. A production model was determined for management and climate factors. The results showed that there was a gap between the Actual yield (farmers' yield) and the potential yield (attainable yield) of 2347. 98 kg. ha-1. Irrigation rate, consumed nitrogen fertilizer, spike density and late planting were 51. 1%, 36. 4%, 7. 9% and 4. 5%, respectively, involved in creating yield gap. Among the farms studied, the farm located in Firoozabad Fahraj with 6272 kg. ha-1 seed yield had the highest seed yield and the farm located in Dolatabad Fahraj with 1116 kg. ha-1 had the lowest grain yield. According to this research, it seems that with proper management of farms and optimization of limiting factors (Irrigation rate, consumed nitrogen fertilizer, spike density and late planting) wheat yield in Bam, Narmashir and Fahraj regions can be increased by 2347. 98 kg. ha-1 as compared to that of the current yields of the farmers, studied.

Introduction: Oilseed rape (Brassica napus) is one of the most important oil plants. While a major portion of vegetable oil is needed from outside suppliers, oilseed rape, because of its high yield, can play a critical role in increasing oil and meal production to meet domestic demands. Golestan Province, especially the eastern part, is one of the most important oilseed rape production areas in Iran. The yield of oilseed rape in this region often averages less than 2 t ha-1. The estimation of yield gap and the determination of the responsible factors are a good approach to improve farmers’ average yield. The present study aims to use the Comparison of Performance Analysis (CPA) method to estimate oilseed rape yield gap in eastern Golestan and to quantify the contribution of each factor to this yield gap in order to reduce yield limitations and improve farmers’ yield. Materials and methods: To identify the limitations of production management, this study was carried out in the 2013-14 and 2014-15 growing seasons. The study was executed in Gonbad, Kalaleh and Galikash, the main oilseed rape production areas in eastern Golestan. In the two years studied, 332 farms were evaluated. The study focused on factors of management methods; soil factors were not considered. All data collected on farm management were analyzed for each city and then for the entire surveyed region with stepwise regression and the CPA method (Torabi,et al., 2011; Nekahi, et al., 2014). Thus, yield gap was obtained as the difference between potential yield and the Actual average yield of local farmers (Van Ittersum and Cassman, 2013).Results and discussion: Whilst Actual yield was 1,081 in Gonab, 1,456 in Kalaleh and 1,728 kg ha-1 in Galikash, the results showed the attainable yield could have been 3,032, 3,516 and 4,032 kg ha-1 in these areas. Thus, the yield gap was 1,951, 2,060 and 2,304 kg ha-1. The Actual yield in the eastern part of the province was estimated at 1,417 kg ha-1, whereas attainable yield could have been 4,800 kg ha-1, resulting in a yield gap of 3382 kg ha-1. The application of nitrogenous fertilizer was one of the main reasons for yield gap in all the surveyed regions. It reduced yield by 25% in this part of the province. Sulfur fertilizer caused 16 percent of the yield gap. Weeds were the other reason. The lack of weeding was responsible for 16% of the yield gap and the failure to apply Lontrel for the management of weeds for 12%. Disease outbreak was another cause. The application of fungicides offsets 16% of the yield limitation. Seed rate, 6%, and sowing date, 4%, also contributed to the gap. Cultivar and seed treatment were each responsible for less than 2%.Conclusion: Weed management, disease, seeding rate, sowing date, cultivar, and seed treatment, were the main causes of yield gap. Therefore with better management and optimal use of nitrogen and sulfur applications, oilseed rape yield can be increased by as high as 3,382 kg ha-1.