Radiation, water and CO2 are three major resources requirement for CROPs growth and development and within a wide range, increasing each of them would increase the biosphere productivity. Higher usage of fossil fuels is increasing the atmospheric CO2 and according to known CROP physiological functions, such conditions should increase the CROPs production. However there is a possibility that these functions forced to modify as well due to such a change in atmosphere. In order to realize such a change, robust MODELS are required which in turn demands high quality data and complete test of the MODELS. Most climate change studies benefit from CROP MODELS however, all MODELS are structured and developed based on current conditions. Our objectives in this study are verification of two CROP MODELS for such a possible future climate change and to find whether there are any required modifications for these CROP MODELS. Required data for this study were obtained from two international studies on rice plant under FACE experiment in Japan and on peanut CROP in USA. Rice experiment included the effects of nitrogen and elevated CO2 and peanut experiment was looking for the effects of CO2 and temperature. Observed data were employed within CSM - DSSAT for peanut and Oryza2000 model for rice plant. The results showed that both MODELS wrongly simulated the magnitude and direction of CROPs responses mostly for interaction of CO2 and nitrogen and/or temperature which indicated the requirement of modification of some relationships in the CROP MODELS in order to be used for any future recommendation under climate change.

A significant course of action to planning agricultural operations and further maintaining and developing performance on a regional scale involves the accurate and timely estimation of CROP yield prior to harvesting using CROP growth MODELS. Modeling dynamic changes during CROP growth can assist researchers in planning CROP management strategies aimed at increasing CROP yield. Such MODELS include several parameters that can be calibrated according to the characteristics of the study area. However, insufficent information on location/spatial-wise components or the lack of  thereof in these MODELS along with uncertainties in parameter values may lead to errors in the estimated outputs. In this light, remote sensing data assimilation can be useful for resolving such complications and evaluating the spatial variability of lands, particularly at the regional scale. Remote sensing can estimate values of input parameters for CROP growth MODELS such as Leaf Area Index (LAI), fCover, biomass, and soil characteristics. This review paper seeks to introduce and compare different methods of remote sensing data assimilation in CROP growth MODELS and examine their advantages and disadvantages. In addition, a literature review conducted in this field can guide the readers in slecting the appropriate CROP growth model, relevant remote sensing data assimilation method, and pertinent state/control variables. The literature review indicates that with new sensors and methods in the estimation of remote sensing state/control variables such as LAI and the development and improvement of CROP growth MODELS, it is possible to improve the accuracy of CROP yield estimation.

In order to determine the phenological differences of some improved rice cultivars in Iran for applying in CROP simulation MODELS, an experiment has been conducted in the research farm of the Rice Research Institute of Iran (Rasht) in 2020 as a randomized complete block with three replications. The experimental treatment consist of six rice cultivars (Rash, Anam, Gohar, SA1, SA6 and M7). Results show that the highest development rate can be observed in development rate in juvenile phase and grain filling phase in Anam cultivar. The minimum and maximum time required to start emergence with 3 and 6 days are in Anam and Gohar cultivars, respectively. The maximum time required to achieve maximum flowering and physiological maturity is obtained with 71 and 103 days in Gohar cultivar. The highest flowering period with 19 and 20 days is obtained in late maturing Rash and Gohar cultivars, respectively. The highest growth degree days (GDD) in beginning of grain filling to maturity stage is observed with 401 GDD for M7 cultivar. The highest growth-day for pre-flowering with 1208 GDD belongs to Gohar cultivar. The highest harvest index is obtained with 50. 91% in Gohar cultivar. The results also show that the single grain weight under ideal growing conditions with 0. 030 g is observed in Gohar and M7 cultivars. The highest plant height belongs to cultivar M7 with 150 cm and the highest total nitrogen uptake is observed in the plant at maturity of Anam cultivar. Overall, the estimated genetic coefficients in different MODELS differ between cultivars and the coefficients vary in the range defined in the model for different groups of maturity. To accurately calculate the genetic coefficients, it is suggested that this experiment should be repeated over several years and in different ecosystems under rice cultivation.

Application of simulation MODELS is a strategy in agricultural water usage management and in predicting the effect of saline water on CROP yield and as well on soil salinity. Recently, FAO has introduced a new version of Aqua CROP model through which one can calculate the effect of irrigation water salinity on CROP yield and on soil salinity. In the present study, Aqua CROP and SALTMED MODELS were evaluated under alternate application of saline vs fresh water for maize as a forage CROP. The required field experiments were carried out in nine treatments (under different conditions of using non-saline vssaline water) in Karaj region. In SALTMED model, R2 values were obtained 0.843 and 0.733 for soil salinity and CROP yield, respectively, while these values amounted to 0.758 and 0.846 respectively, for Aqua CROP model. Relative error in Aqua CROP model varied between 2.9 and 30.8% in CROP yield estimation and between 5.9 and 45.8% in soil salinity estimation. The relative error in SALTMED model ranged from 0.9 to 24.7% to estimate CROP yield while ranging from -2.2 to 38.2% in estimation of soil salinity.

The estimation of reference evapotranspiration (ETo) is of great importance due to its applications in water resource management as well as irrigation scheduling. Difficulties associated with using lysimeters have encouraged researchers to use various ETo MODELS, while the shortage of actual radiation data seems the main obstacle for users of radiation-based MODELS. In this research the output of four radiation-based evapotranspiration MODELS including: Penman-Montieth-FAO56 (PMF56), Penman-Montieth FAO-Irmak (PMFI), modified Jensen-Haise (JH1), and Jensen-Haise (JH2) are evaluated for a cold semi-arid climate. The daily ETo values were generated for 16 different scenarios and the results were compared against a two-year lysimeter data during the growing season (May to November). Deviations of model results were investigated using mean of R2, RMSE, MBE and t-test criteria. The results indicated that the JH2 model which uses radiation model of Daneshyar, can generate the most accurate ETo values (R2>0.85, P<0.05) in cold semi-arid climates. Although, the Angstrom radiation MODELS are widely being used to generate radiation data in recent years, this research showed that other radiation MODELS can provide more accurate radiation data for radiation-based ETo MODELS. In places with lack or shortage of meteorological data, using an accurate radiation model might significantly reduce the errors generated by certain ETo MODELS. The mentioned radiation MODELS and ETo MODELS should be examined for other climates.

Increasing CROP production depends on supplying CROP water demands, thus, accurate estimation of CROP water requirement helps not only to CROP production, but also is effective in the management of water resources. Based on this, the purpose of the present research was to investigate the estimates of reference evapotranspiration from SEBAL and METRIC MODELS using satellite imageries of Landsat 7 and 8 and Terra in the Qazvin plain. In the first step, the estimates of METRIC and SEBAL MODELS with a total of 10 images obtained from MODIS sensor, Terra satellite, and ETM+ sensor, Landsat 7 satellite, were evaluated using lysimeter data for grass reference CROP in 2001. Estimates of MODIS sensor with r=0.88, RMSE=1.91, and SE=0.85 mm/day and Landsat ETM + with r=1.00, RMSE =0.91, and SE=0.09 in METRIC model were closer to the lysimeter data compared with the SEBAL model. In the next step, the results of the METRIC and SEBAL MODELS obtained from OLI & TIRS sensor images of Landsat 8 satellite were evaluated with the results of METRIC model on ETM+ due to lack of lysimeter data at the time of checking. Evaluation of the results indicate that the METRIC model with r=0.96, RMSE=0.28 and SE=0.29 mm/day may be recommended as a superior model compared with SEBAL model, for estimating reference CROP evapotranspiration in the Qazvin plain.

The MODELS ORYZA2000, SWAP and WOFOST simulate the growth and development of rice under conditions of potential production and water limitation. The MODELS were evaluated against a data set of field experiments. The study was laid out in RCBD with three replications for one traditional landrace, Hashemi, carried out in 2005 at the Rice Research Institute of Iran in Rasht. The irrigation managements were: I1 (continuous irrigation); I2, I3, and I4 (irrigation at 1, 3 and 5 days, respectively, after disappearance of ponded water) and I5 and I6 (irrigation at 5 and 8 day intervals, respectively). In this paper, simulated and measured leaf area index (LAI), biomass panicles, total aboveground biomass and yield by adjusted coefficient of correlation (R2), and absolute and normalized root mean square errors (RMSE) were compared. On average, RMSE total aboveground biomass was 653 kg/ha for ORYZA2000, 458 kg/ha for WOFOST and 589 kg/ha for SWAP. RMSE biomass panicles biomass was 375 kg/ha for ORYZA2000, 284 kg/ha for WOFOST and 335 kg/ha for SWAP. RMSE LAI was 0.41(-) for ORYZA2000, 0.53(-) for WOFOST and 0.5(-) for SWAP. ORYZA2000, in contrast to WOFOST and SWAP, simulated the yield of rice well.

Reference CROP evapotranspiration (ETo) is an important parameter for determining CROP water requirements in the FAO method, and hence, its accurate estimation is of crucial importance in optimizing CROP water use and water resources management. In this research where conducted in Haji Abad region of Hormozgan province for four years, weekly evapotranspiration from grass as a reference CROP was measured using a drainable lysimeter. The ETo was also estimated using eleven MODELS including FAO Radiation, Evaporation Pan, Blaney-Criddle, Penman and Penman-Monteith and Papadakis, Ivanov, Jensen-Haise, Hargreavse-Samani, Tork, and Makking MODELS. The estimated ETo values from different MODELS were compared with the data measured from lysimeter using linear regression, root mean square error, mean bias error, index of agreement and variance of the distribution of differences. Results showed that FAO Penman, Ivanov, Papadakis and Jensen-Haise MODELS overestimated ETo, but the FAO evaporation pan, Hargreaves-Samani, Turk and Making MODELS underestimated it. The results also indicated that the FAO Blaney-Criddle and FAO Penman-Monteith MODELS had respectively higher accordance and homogeneity with the real data measured from lysimeter and can predict ETo with higher accuracy than other tested MODELS. Therefore, these MODELS are recommended, respectively, as the most appropriate MODELS to estimate ETo in Haji Abad region and the areas having the same climate.

The purpose of this study is to identify factors influencing the production system sustainability by summer CROP farmers. In this regard, the spatial Probit MODELS and Bayesian approach was used. The data of 250 summer CROP farmers in Khuzestan province based on cluster random sampling were collected in winter 2015. To calculate Bayesian coefficients the Gibbs sampling and Metropolis–Hastings algorithm were used. A Lagrange Multiplier test for spatial error dependence [LM(err)] and a Lagrange Multiplier test for spatial lag dependence [LM(lag)] to extract the appropriate model were used. According to the results, both MODELS were statistically significant with 99% probability. Thus, both MODELS could be used in interpreting the results. Results of the estimation of both spatial MODELS, respectively, showed that the variables of income (0.812, 0.659), sustainability knowledge (0398, 0.465), CROP yield (0.457, 0.765), participation in extension class (0.427, 0.486), educational level (0.562, 0.454), exploitation system (0.786, 0.576) and the spatial autoregressive coefficient (0.829, 0.739) had significant role on production system sustainability.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language MODELS and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.