Yield gap analysis provides an essential framework to prioritize research and policy efforts aimed at reducing yield constraints. To identify options for increasing chickpea yield، the SSM-chickpea model was parameterized and evaluated to analyze yield potentials، water limited yields and yield gaps for nine regions representing major chickpea-growing areas of Razavi Khorasan province. The average potential yield of chickpea for the locations 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. Also، the average irrigated and rainfed actual yields were respectively 64% and 79% less than simulated potential and water limited yields. Maximum and minimum yield gap between potential yield and actual yield were observed in Quchan and Torbat-jam respectively. Generally، yield gap showed an increasing trend from the north (including Nishabur، Mashhad، Quchan and Daregaz regions) to the south of the province (Torbat- Jam and Gonabad). In addition، yield gap between simulated water limited potential yield and rainfed actual yield were very low because both simulated water limiting potential and average rainfed actual yields were low in these regions. Yield gap analysis provides an essential framework to prioritize research and policy efforts aimed at reducing yield constraints. To identify options for increasing chickpea yield، the SSM-chickpea model was parameterized and evaluated to analyze yield potentials، water limited yields and yield gaps for nine regions representing major chickpea-growing areas of Razavi Khorasan province. The average potential yield of chickpea for the locations 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. Also، the average irrigated and rainfed actual yields were respectively 64% and 79% less than simulated potential and water limited yields. Maximum and minimum yield gap between potential yield and actual yield were observed in Quchan and Torbat-jam respectively. Generally، yield gap showed an increasing trend from the north (including Nishabur، Mashhad، Quchan and Daregaz regions) to the south of the province (Torbat- Jam and Gonabad). In addition، yield gap between simulated water limited potential yield and rainfed actual yield were very low because both simulated water limiting potential and average rainfed actual yields were low in these regions.

Human diets strongly rely on wheat (Triticum aestivum L.). Its production has increased dramatically during the past 50 years, partly due to area extension and new varieties but mainly as a consequence of intensified land management and introduction of new technologies. For the future, a continuous strong increase in the demand for agricultural products is expected. It is highly unlikely that this increasing demand will be satisfied by area expansion because productive land is scarce and also increasingly demanded by non-agricultural uses. The role of agricultural intensification as key to increasing actual crop yields and food supply has been discussed in several studies. However, in many regions, increases in grain yields have been declining Inefficient management of agricultural land may cause deviations of actual from potential crop yields: the yield gap. At the global scale little information is available on the spatial distribution of agricultural yield gaps and the potential for agricultural intensification.
Actual yield is mostly lower than potential yield due to inefficient management and technological that difference between these yields is considered as yield gap. Understanding of relative share of every management factors in yield gap could be as one of the important keys to reduce gap and close actual yield to potential yield.
In order to evaluate the amount of wheat yield gap and also relative share of management and technological variables in yield gap, frontier production function was used which is a multi-variable regression. The frontier production function to be estimated is a Cobb-Douglas function as proposed by Coelli et al. (2005). Cobb-Douglas functions are extensively used in agricultural production studies to explain returns to scale. We propose a methodology to explain the spatial variation of the potential for intensification and identifying the nature of the constraints for further intensification. We estimated a stochastic frontier production function to calculate global datasets of maximum attainable grain yields, yield gaps, and efficiencies of grain production at. Applying a stochastic frontier production function facilitates estimating the yield gap based on the actual grain yield data only, instead of using actual and potential grain yield data from different sources. Therefore, the method allows for a robust and consistent analysis of the yield gap. The factors determining the yield gap are quantified at both global and regional scales.
For this purpose, climatic information and wheat yield of different provinces were obtained from Iran meteorological organization and Agriculture Jahade organization, respectively. Wheat potential yield in different provinces was simulated by WOFOST model. Wheat gap was gained by difference between actual and potential yield in different provinces. Relative share of climatic variables in potential yield and also relative share of management variables included irrigation, fertilizer application, mechanization, pesticide application and manure in wheat yield gap was calculated by frontier production function.
The results showed that the effect of precipitation and radiation on wheat potential yield was positive and the impact of temperature was negative. Precipitation had the highest impact on wheat potential yield among other climatic variables. The range of wheat yield gap was from 1646 to 4470 kg ha-1 and 29 to 58% in Iran. Generally, the effect of all management variables on wheat yield gap was negative so that wheat yield gap was reduced by improving of these variables. Among studied management variables, irrigation had the highest effect on yield gap reduction, especially in dry-warm climate and fertilizer application was the second factor which had high effect on yield gap reduction. Therefore, to reduce wheat yield gap in Iran, irrigation management and fertilizer application should be considered.
Between studied climate variables, the relative contribution of temperature and rainfall was higher on wheat yield potential compared with radiation in all provinces except the province of Zanjan, Golestan, Gilan and Mazandaran. The highest gap yield (4470 kg ha-1) was assigned to Ilam and Mazandaran provinces. Irrigation and fertilizer application were the more affective variables in yield gap induction.

Potato (Solanum tuberosum L.) accounts for the largest share in production of food products after wheat, rice and corn, which plays an important role in the nutrition and food basket of the world. Currently, the issue of food security and supplying is very important in different parts of the world and thus prediction of its demand that is increasing. Also, growing population will intensify this issue too. A key strategy to overcome the nutritional challenge of the growing population of the world is eliminating the gap between the current achievement in farms and the yield which can be achieved by using the best cultivars compatible with the environment and the best water, soil and plant management methods.

In this study, potential yield of potato was estimated using the SSM-iCrop2. then the production and yield gap of potato were investigated with two methods of Global Yield Gap Atlas (GYGA) and Arc GIS software by interpolation method for Golestan province. For this aim, the information of potato cultivation management in the province level and the daily data of 23 synoptic weather station as well as their soil data were used. Meanwhile, potential yield gap analysis protocol (GYGA protocol) was used to identify the main weather stations named reference weather stations (RWSs) and climates where potato is planted in Golestan province. Potential yield of potato was estimated within the area covered by each RWS, and then scaled up to the province level. In order to implement the interpolation method in ArcGIS software, initially, potential yield of 23 stations in the Golestan province was estimated. Afterwards, the potential and actual yield in the whole province was estimated using Kriging and IDW methods, respectively.

According to the Agricultural Jihad Report, the actual yield of Golestan province (a ten-year average) was reported as 22 t.ha-1, while the actual yield calculated based on the interpolation method was estimated equal to 22 t.ha-1. However, the actual yield calculated using the global yield gap atlas (GYGA) method was estimated as 20 t.ha-1. Heigh yield of potato in the province was reported by farmers up to 55 tons, which was considered as potential in the province to be compared with GYGA and interpolation methods, so that in the GYGA method, 52 t.ha-1 and in the interpolation method, finding was 42 t.ha-1. yield gap and relative yield in the GYGA method (33 t.ha-1 and 38 percent), and interpolation (20 t.ha-1 and 52 percent) were obtained. Also, in GYGA method, the values ​​were estimated using only two stations, but in the interpolation method the province's yield was estimated using 23 stations and daily meteorological data. The GYGA protocol is a bottom-up approach used by Hochman et al. (2016) to assess the variation of national yield from climatic zoning to analyze similar agro-cluster groups. So far, several studies have been done using this protocol. Gobbett et al. (2017) showed that wheat lands in Australia are located in six key climatic regions. They selected 22 reference stations in this area and calculated the limited water potential yield using the APSIM model (for the years of 1996 to 2010).

The results showed that both Global yield gap Atlas (GYGA) and interpolation method had accurate estimation of actual and potential production and yield, but those values obtained using statistics and 23 weather stations in the interpolation method, and using just 2 weather station (Hashemabad and Gorgan stations) in the Global Atlas method. As previously mentioned, the GYGA method is designed to estimate the yield gap at the national level, which can calculate the potential yield, production, and yield gap in a wide range even with the least amount of information. One of the advantages of GYGA protocol is that the results obtained at any point in the world are comparable to other areas and cultivations of that particular product. Based on the comparison made in this study, it was concluded that the Global yield gap Atlas method should be used to estimate the yield of the country.

To realize global food demand by 2050 world cereal production should be increased up to 49% compared to 2006. This level of production could be achieved by annual yield increment of 1.16%. However, the current rates are much lower. At the same time, there is a very restricted area to increase cultivated lands because of resource limitation, provided that increase in crop yields is the main option to sustain food security. Potential yield (YP) could be achieved when limiting and reducing factors are completely absent during crop growth. YP is an indicator for the yielding capacity of a given environment and management system and estimating the difference between YP and actual yield, known as yield gap, is crucial for improvement of crop production systems at regional or national scale. In this study yield gap and its temporal trend for sugar beet, irrigated and rainfed wheat are estimated over Khorasan Razavi province based on the method developed by Global Yield Gap Atlas. Following the protocol provided by Global Yield Gap Atlas, Khorasan province was clustered into agroclimatic zones using the proposed indices (cumulative degree days above 0 ºC, aridity index and temperature seasonality) based on 10 years (1384-1393) weather data. YP of sugar beet and irrigated wheat for the study period in the climatic regions was first estimated for selected cities within each region using LINTUL model and finally the simulation results were up scaled from cities to region and from regions to the whole province. The model was cross-validated against measured data using leave-one-out (LOO) method to increase accuracy of predictions. Potential yield of rainfed wheat (YW) was estimated from frontier production function which was fitted to yield data over a wide range of annual precipitation. Yield gap (YG) of the studied crops was estimated as the difference between potential (YP) and actual yields (YA) for each region and over the 10-year period. In addition exploitable gap (YG85%=85%YP-YA) was also calculated. The accuracy of LINTUL model for simulation of sugar beet and irrigated wheat yields was considerably increased after cross validation and the prediction error was reduced by 6.5 - 7.8%. Mean YP of irrigated wheat in the climatic region 1 (temperate, semi-dry), 2 (hot, dry) and 3 (temperate, dry) was respectively, 7248, 6478 and 7852 and for the whole province 6936 kg ha-1. Time trend of YP for irrigated wheat was not significant in 3 climatic regions however, high annual variation of YP was found over the studied period. Results indicated that up to 74% of this variation was accounted for by changes in the effective grain filling period in response to temperature. YG85% of irrigated wheat in all climatic regions was increased up to 4 t ha-1 during 1384-1388 but decreased later on so that relative gap was 0.48-0.50 of YP in 1993. Average YW of rainfed wheat in the climatic regions of the province was estimated as 2000-2800 kg ha-1 with a negative trend due to decreased precipitation, the highest negative slope in YW (59 kg ha-1 y-1) was found in the hot dry region. Rainfed wheat showed an extremely high yield gap in all climatic regions and mean relative yield gap (YG/YW) was estimated as 0.75-0.80 over the province. Mean YP of sugar beet in different climatic regions of the province was estimated from 78 to 88 t ha-1 with the lowest potential in hot-dry region. However, declining trend was found in the yield gap of sugar beet in all studied regions with the highest gap filling rate of 1.44 t ha-1 y-1 in temperate-dry region. Simulated YP of sugar beet and irrigated wheat were higher in temperate-semi arid regions of the province and lower in hot-dry regions. However, cold-semi arid regions had the highest YW of rainfed wheat. When up-scaled over the province, YG85% was about 50% of YP for irrigated wheat and sugar beet and 25% for rainfed wheat. It was concluded that closing yield gap of sugar beet and irrigated wheat would be possible mainly by improving management practices however, for rainfed wheat breeding strategies should be considered as the first priority.

  Potential production condition is defined as situation where water and nutrients are supplied in ample with no damage from weeds, pests and diseases. Under this situation potential yield (Yp) which is defined by solar radiation, temperature, CO2 concentration and genotypic characteristics of the crop species could be achieved. Closing the gap between Yp and actual yield (Ya) harvested by farmers is considered as the main challenge of agronomic sciences all around the world. Crop simulation models provide a powerful tool for prediction of Yp at regional and national scales. However, the accuracy of these models is highly dependent on the quality of input data which are usually not fully available even in developed countries. Simplified models designed based on few simple equations may be considered as an alternative where accurate data sets are lacking. In this research, Yp of wheat and sugar beet were estimated using some simple methods and the results were compared with those of complex simulation models under the same climatic and management conditions in Khorasan province, north east of Iran. Potential yields of wheat and sugar beet were estimated by three simple calculation methods including FAO method (FAO, 1981), modified FAO, FAO-M (Versteeg and van Keulen, 1986) and RUE-based method (Nonhebel, 1997) at three different regions (Torbat, Mashhad and Neishabor) in Khorasan province. In these methods total above ground dry matter (DM) is calculated using two equations and Yp is estimated as the product of DM and harvest index with minimum weather data requirements. In addition, Yp for both crops and at the same locations was predicted using LINTUL and SUCROS simulation models which were previously calibrated for yield estimation of wheat and sugar beet at regional level. The accuracy of predicted potential yields by five methods was compared statistically against the measured yields obtained from the field experiments and high yielding farms at the studied regions. Mean observed yield of wheat over the three studied regions was 7.18 t ha-1 and the estimated potential yield in the same regions using two simulation models and three simplified models was ranged between 6.92 and 7.63 t ha-1. Prediction error for the simulation models was 1.39 and for simple methods less than 5% (4.64%). Relative root mean squared error (RMSEn) for the simple methods was higher than that of complex models. However, it was between 7.11 and 10.16 % of the mean measured wheat yield which could be statistically considered as reasonable. Calculated values of modeling efficiency (ME) were positive and higher than 0.60 except for FAO-M method (ME=0.48). Measured sugar beet yield averaged over regions was 82.5 t ha-1 and estimated potential yield by different methods was between 89 and 91 t ha-1. Simple calculation methods had lower accuracy in predicting sugar beet yield compared to simulation models but RMSEnof simple methods never exceeded 11.5% showing statistically acceptable prediction. Cross validation indicated significant correlation between the results of three simple methods and that of complex simulation models. Results of this study showed that potential yields of different crops can be estimated accurately using simple calculation methods with minimum weather data requirements. Such methods may be used as an alternative for agroecological zoning and yield gap analysis at regional level where calibrated simulation models and complete daily weather data sets are not available

Crops yield can be considered in different situations, which is called different production levels. Potential yield level is that determined by radiation, temperature and cultivar traits without limitations of biotic or abiotic factors; while attainable yield level is that limited by water or nutrient supply, and actual yield level is that determined in the present of the limiting (water or nutrient) and reducing factors (such as weed, harmful insects and plant diseases). The yield gap is the difference between the potential yield level and actual yield level. Identifying the yields at different production levels and quantifying the yield gaps through field experiments may involve many years of data collection on which to make meaningful inferences. Crop simulation models are an alternative tool for analyzing interactions between water and nitrogen availabilities on yield generation. The objectives of the present study was to estimate potential yields and yield gaps for irrigated wheat using the CERES-Wheat model, and other limiting and reducing factors in the main wheat growing regions of Kermanshah province. The study was conducted at 3 locations in Kermanshah province, which is located in west of Iran. Historical weather data for 1999 to 2016 were obtained from the Iran Meteorological Organization for the 3 locations. The weather data included daily solar radiation (MJ m-2 d-1), daily maximum and minimum temperatures (°C), and daily rainfall (mm). The CERES-Wheat model was calibrated for Pishgam cultivar of wheat and validated for a main wheat growing regain to estimate yield gaps in some locations of Kermanshah province in west Iran. In order to calibrate and validation of the model, in another study, leaf area index, phenological growth stages, total dry weight yield and grain yield were used. The validated model was used to simulate long-term yield under three management conditions (potential, water-limited, nitrogen-limited). The experiments were conducted in the Campus of Agriculture and Natural Resources Field at Razi University, Kermanshah, Iran (34◦19´N, 47◦50´E, altitude 1320 m) on soil classified as Inceptisol typic during 2014-2015 and 2015-2016. The treatments were included 4 levels of nitrogen fertilizer application (90, 180, 300 and 360 kg ha-1 urea). The simulation results indicated that averaged simulated potential yield was 8.9 t ha-1, while water and nitrogen limitation yields were 8.0 and 7.1 t ha-1, indicating 10.3% and 20.2% reduction in wheat yield, respectively. The potential yields changed spatially due to changes in temperature and solar radiation. The average actual yield was 5.5 t ha-1 which was 3.4, 2.7 and 1.8 t ha-1 less than potential, water limitation and nitrogen limitation yields, respectively. There was fairly large gap between the actual and the potential yields (about 3.4 t ha-1). When averaged over years, total yield gap obtained for Kermanshah was 2.7 t ha-1, for Kangavar was 3.7 t ha-1, and for Ravansar was 3.8 t ha-1. Across locations, contribution of yield gap from water limitation was 26%, for nitrogen limitation was 51.3% and for other limiting and reducing factors was 22.7%. It can be concluded that the management timing of nitrogen application might reduce yield gap across locations. On the other hand, improved irrigation methods might lead to improved actual yield through preventing of the water and nitrogen leaching which it can reduce the total yield gap. In this study, only the role of water and nitrogen limitation was evaluated. The data collected from questionnaires showed that the yield gap of wheat was affected by the unsuitable of crop management practices, including optimum planting date, and pest and weed management that were not studied in the simulations.

Wheat (Triticum aestivum L.) known as a main crop in Iran. It is the main source of calories and protein which directly provides 37 percent of the food calories and 40 percent of daily protein for people in Iran. Breeding to produce new cultivars is always an important way to increase crops yield. New cultivars breeding is a very complex process because there is an interaction between climate and genotype and the time is limited to produce new cultivars adapted to new climates. The target trait identification can accelerate new cultivar breeding process. The objectives of this study were to explore the potential benefit of irrigated wheat traits over the country to increase the yield.

This study was performed at potential yield simulation using SSM-Wheat crop model to evaluate different traits impact on irrigated wheat potential yield in Iran. For this purpose, the protocol presented by Global Yield Gape Analysis (GYGA) was used to identify the same climate zones and the main weather stations for irrigated wheat in Iran. The potential yield of irrigated wheat was simulated by SSM-iCrop model for the area covered by each main weather stations. The average potential yield was calculated at the country level by scaling up the simulated results within the area covered by weather stations using the GYGA protocol. All the simulations and calculations were done for existing cultivars and for the cultivars with desired plant traits, identified in this study, under current and future climates. The effect of desired plant on potential yield was quantified by comparison of simulation results between existing cultivars and the cultivars with desired plan traits. Future climate (2055) scenario were created for the sites using the baseline 1986-2005 and the projections for delta mean air temperature (and precipitation) which is the difference between the future air temperature (and precipitation) and baseline air temperature (and precipitation). Deltas of air temperature and precipitation were obtained from the international panel on climate change report which it used 42 GCM model outputs under RCP4.5 climate change scenario to calculate them.

In this study, the effect of increasing and decreasing of biological days from tillering to stem elongation, biological days from anthesis to philological maturity, the rate of canopy development and radiation use efficiency on irrigated wheat potential yield were evaluated. Increasing biological days from anthesis to philological maturity increased the potential yield in all the regions under current (15.3 %) and future climates (16.8%). The potential yield gain from increasing radiation use efficiency was 14.7% under current climate and 13.7% under future climate. The effect of decreasing biological days from tillering to stem elongation, biological days from anthesis to philological maturity, the rate of canopy development and radiation use efficiency on the potential yield were negative. Monpara (2011) reported that increasing duration of grain filling period was an effective trait to increase wheat yield in India. Yang et al. (2008) demonstrated that the yield of rice increased with increasing cumulative radiation receiving during grain filling period. There was positive correlation between cumulative radiation receiving during grain filling and grain filling duration. With longer stay green duration, the potential yield of wheat increased thereby raising photosynthesis during wheat grain filling period (Spano et al., 2003).

Increasing radiation use efficiency positive effect on potential yield in the regions with warmer climate was higher than the region with lower average temperature over the year. Increasing radiation use efficiency had negative effect on potential yield in some cooler regions. Increasing biological days from tillering to stem elongation just had positive effect on potential yield in the region with warmer climate and its effect was negative in the regions with cool climate. The faster canopy development had no significant effect on potential yield.

In crop zonation studies potential yield is usually estimated by simulation models where management inputs are defined as recommended by research stations. However, these recommendations are not necessarily in accord with the local situation. Therefore, the prediction of simulation models is subjected to uncertainty or risk. In this research the risk of wheat yield estimation due to management inputs in Khorasan provinces is analysed by application of Monte Carlo simulation. Potential wheat yield was estimated by WOFOST model which was previously validated against the experimental data. The results of sensitivity analysis indicated that change in planting date in order of ±50 days relative to a reference sowing date resulted in a drastic variation in estimated wheat yield. However, potential wheat yield was not sensitive to variation in planting density in the range of ±50% of the reference density. Therefore, risk analysis was performed on planting date. 100 random planting dates were generated using a random data generator software and the WOFOST model was run for each generated date during 10 climatic years (1375-1385). Using the simulation results probability distribution of potential wheat yield was calculated for recommended planting date and dates with ±15 or ±30 days deviation. The results showed that risk of wheat yield in response to change in planting date was different between provinces. In the Northern Khorasan province earlier planting by 15 days compared to the average recommended date led to risk reduction while wheat yield was more risky when planting date was delayed by 30 days. In Khorasan Razavi province the recommended planting date resulted in lower risk but yield risk was increased by earlier planting. In Southern Khorasan province wheat yield was subjected to minimum risk and higher stability when the recommended planting date was delayed by 30 days. It was concluded that the low potential wheat yield particularly in Southern Khorasan could be due to management practices and higher yield with low risk could be achieved with improvement of the current management operations.

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. Crop potential yield is one of the parameters that can be used to calculate the yield gap and to 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 of Qazvin plain has been determined by agronomic method. 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 a high yield in crops such as sugar beet, tomato and alfalfa. Given that in the current situation of operation of the irrigation network, it is very difficult to achieve potential performance, in such cases, the gap between actual performance and achievable performance should be reduced.

It is essential to understand crop yield potentials and exploitable gaps to detect yield constraints of crops. Yield gap analysis is a method to evaluate our systems distance from actual capability of a region to produce a given crop which will direct researches and help for cropping patterns design and programming. Moreover، knowledge on a region capability could result in investigating on given crop. Thus، this research was aimed to quantify water-limited potential yield، comparison of actual and potential yield and yield gap analysis of chickpea production in Zanjan province. Also Geographical Information System (GIS) approach was used to produce agroecological zoning maps. Averaged water-limited potential yield in Zanjan province was assessed as 727 kg ha-1 (varied from 559-1075 kg ha-1) using a simple simulation model of chickpea. With considering averaged actual yield as 408 kg ha-1 (average of three years، from 2009-2011)، mean yield gap was evaluated as 43% (varied from 24-59%)، which its average value is equal to 320 kg ha-1 (varies from 157-475 kg ha-1). The most important finding was the possibility of considerable increasing of yield in studied province in comparison with current yield. Our results on yield gap revealed that with covering of 80% potential yield، it is possible to increase yield from 5 to 95% (48% in average). Improvement of sowing، protection and harvesting managements are the main and lowest-cost options to fill the chickpea yield gap in Zanjan Province.