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.

Irrigation management plays an important role in the performance and productivity of RICE water consumption. This experiment was conducted to investigate the effect of irrigation time on YIELD and of Ratoon Hashemi cultivar in the form of a factorial design with three replications and in three crop years at the RICE Research Institute (Rasht). Irrigation treatment was at three levels of permanent flooding (I1), capillary cracking (2 mm) (I2) and coarse cracking (5 mm) (I3). The results of compound analysis of variance showed that all quantitative and qualitative traits of RICE ratooning were significantly affected by the test years. In general, due to the right temperature and hours of sunshine, ratoon growth conditions in the first year of the experiment were better than in the other two years, which positively affected plant height, cluster length and 1000 grain weight, increasing grain YIELD by 1122 kg/ha. The effect of irrigation management on ratoon YIELD and YIELD components also showed that coarse crack irrigation increased YIELD by 463. 4 kg/ha compared to other treatments. Also, the highest plant height of 104. 8 cm was obtained from flood irrigation. Irrigation management was not effective on other traits such as 1000-grain weight, straw YIELD, and cluster length and harvest index. The highest water productivity in the first year was 0. 69 kg/m3/ha and the lowest in the third year was 0. 41 kg/m3/ha. Also coarse crack irrigation treatment compared to permanent flood irrigation treatment increased water productivity by 65%.

In this paper, using image processing and remote sensing techniques, RICE YIELD estimation is performed for a region in Mazandaran province, North of Iran. Four Liss-III images from IRS-ID satellite have been used. Two of the images are from RICE cultivation period and other two are from different times of the year. Geometric corrections have been performed on the images using ground control points. At first image pixels were classified to "RICE" and "non-RICE" classes using a2- class MLP neural network. Then RICE farms were classified as "high YIELD" and "low YIELD" differentiations of RICE cultivations using another MLP neural network. Estimation of RICE YIELD for each of these classes of RICE farms were then accomplished using an REF neural network model. Finally using the three mentioned neural network models, the estimation of RICE YIELD for the whole region was performed Good results have been obtained from the models which are presented in the paper.

In order to evaluate the performance of the CERES-RICE model in RICE biomass and grain YIELD estimation in large scale, this study was carried out in paddy fields of Guilan, Iran. First the model was calibrated and evaluated using data from a research at RICE Research Institute of Iran, then the model accuracy was assessed in 26000 ha of Some Sara region.110 common farmers fields were chosen, the soil, water, crop samples and other needed information were taken. The model was ran for each field. The results showed that the Error of YIELD and biomass estimation in research station were below 10 percent. The results in large scale showed that the model estimation of YIELD and biomass could be categorized in three different parts. While in fields with average biomass and YIELD observation the setimation error was negligible. Underestimation was observed in the fields with the YIELD above the average of the region while in high YIELDing fields overestimation was observed. The Normalized Root Mean Square Error was about 22.7 and 21% for biomass and YIELD prediction. Although in some cases the error was about 50%, in 75% of the region it was in range of 0-10%, and in 95 % of the region it was in range of 0-20%. Water productivity based on water used in most of the fields was in the range of 0.5-0.6 kg/m3 and the water productivity based on transpiration was 0.85-0.9 kg/ m3. The model showed a satisfactory accuracy to estimate both YIELD and biomass in large scale paddy fields.

In order to determine the relationship between grain YIELD and YIELD components, an experiment was conducted to evaluate 36 genotypes of RICE using a simple lattice design, in the year 2000. In this study 17 traits including grain YIELD, number of panicle per plant, number of filled grain per panicle, 100-grain weight, leaf length, leaf width, leaf area, plant height, culm length, amylose content of the grain, gel consistency, panicle weight, grain length, grain width, grain shape, days’ to 50% of flowering and maturity were assessed on 10 random plants from each plot. Analysis of variance showed a highly significant difference among the genotypes for all the traits. Also the results showed that the grain YIELD had a positive and significant genotypic correlation with number of panicle per plant, number of filled grain per panicle, grain weight, grain maturity and days to 50% of flowering. Stepwise regression analysis showed that the maximum variation in grain YIELD could be attributed to the number of panicle per plant, number of filled grain per panicle, 100-grain weight and leaf length. The results of path analysis indicated that the number of panicle per plant and number of filled grains per panicle were the most important components of high direct effect on grain YIELD. Factor analysis resulted in six factors that determined 83.6% of the total variation. The second factor along with the sixth factor accounted for 27.3% of total variation and was designated as “YIELD and YIELD component” factor. The first, third, fourth and fifth factors were identified as morphological traits, growth and maturity stage, grain appearance and grain cooking quality” factors respectively.

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.

This experiment was carried out with the aim of attaining a suitable variety and culture method and optimum culture condition in a randomized complete block design in 3 replications with method of culture factor (traditional, Mechanical and direct seeding), variety factor (Barakat, Alikazemi, Hashemi) and condition of culture factor (concurrent RICE-fish and mono culture of RICE) in Astara in, 2010. The results showed a significant difference between the studied varieties for all traits except grain number per panicle. Between the method and culture conditions there was a significant difference for all traits. Interaction effect between variety and culture method did not have any significant difference for traits except panicle weight, tiller number per bush and straw weight. Interaction effect between method and culture condition had a significant difference only for straw weight. On the basis of the results of the experiment, the Hashemi variety for more paddy YIELD with mechanical culture of RICE seedling method in concurrent RICE - fish culture was suitable variety, method and culture condition.

Today, global warming is one of the most important human concerns in the fields of economy, energy and environment. The most important element in global warming is the burning fossil fuels that release the greenhouse gases including CO2. The researches by the Intergovernmental Panel on Climate Change show that global warming will rise more than 2 degrees centigrade by the end of the century with respect to the pre-industrial era, if no effort is made to reduce CO2 emissions. Since the MENA region is one of the most vulnerable regions in the world due to climate change, so the main purpose of this study is to assess the effects of climate change on the economic variables in MENA region until 2105. For this purpose RICE model is used which is based on the computational general equilibrium model. The results indicate that the global average temperature will rise to 4. 49 degrees centigrade until 2105 year in comparison to the pre-industrial era, if no policy is implemented to prevent rising temperatures. The trend of regional GDP and consumption are similar and will increasingly move. In the long-run, the capital accumulation will increase in the upper-middle income, the lower-middle income and the high income countries, respectively,while employment will increase in the lower-middle income, the upper-middle income and the high income countries, respectively. Environmental damage is the most in the high income countries in comparison to the other groups in MENA region.

2In order to investigate the possibility of improvement of YIELD, YIELD components and the concentration of some nutrients in RICE leaves (Tarom variety) by using RICE straw and its biochar, a factorial experiment was conducted in the form of a randomized complete block design in nine treatments and three replications in the farm of Sari Agricultural Sciences and Natural Resources University, Sari, north of Iran, in 2021. The first factor was RICE straw (S) at three levels of zero, 2 and 4 tons/ha and the second factor was RICE straw biochar (B) at three levels of zero, 1 and 2 tons/ha. The maximum concentration of potassium (1. 22%), nitrogen (4. 73%) and iron (208 mg/kg) in the flag leaf was observed when RICE was grown in the high levels of RICE straw and particularly its biochar. Also, the concentration of silicon, manganese and zinc in the flag leaf increased with the increase in the levels of RICE straw and its biochar. By increasing the use of RICE straw and its biochar, the number of tillers, plant height, panicle length, weight of 1000 seeds and the number of filled seeds increased significantly. The combined use of RICE straw and its biochar increased the RICE grain YIELD, indicating a 1. 78 times increase in the treatment of 2 tons/ha of RICE straw biochar + 4 tons/ha of RICE straw (S2B2) compared to the control. Therefore, it can be concluded that the application of 2 tons/ha of biochar and 4 tons/ha of RICE straw can lead to the maximum grain YIELD by increasing the concentration of nutrients in the flag leaf and improving the growth and YIELD components of RICE.