This study was conducted to analyze the Total Factor Productivity in onion production of nine major provinces using Malmquist index. The results indicated that, the country wide Productivity decreased about 9.6 % during the study period. The amount of reduction was not the same in different regions and provinces. It was in East Azerbaijan (0.07), Esfahan (0.06), Sistan and Baluchestan (0.04) and Fars (0.06) lower and in the district of southern Kerman (0.13), Khorasan (0.1), Khuzestan (0.12), Zanjan (0.14) and Hormozgan (0.14) had been greater. Among the provinces studied, the greatest improvements in technical efficiency belonged to the farmer of Efahan province and the onion growers of Sistan & Baluchestan province had experienced the least decline in technology, so it is a necessity to introduce new methods and uses other regions successful results to increase the share of Productivity in onion production growth.

This paper investigate the long-run relationship between the Total Factor Productivity and the labor Productivity in Iran during 1970-2007 and use Autoregressive Distributed Lags (ARDL) and Bound tests approaches. Results explain that there is a mutual long-run relationship between Total Factor Productivity and labor Productivity, and Total Factor Productivity and labor Productivity have positive and significant effect on each other.

In this paper Total Factor of Productivity growth in corn production was estimated for 7 years period (2000-2007) by using data envelopment analysis. Dividing Malmquist index to effects including technical efficiency changes and technological efficiency changes (pure technical efficiency, scale efficiency) and showed Malmquist index changes, its reasons and subsequently justified Total Factor Productivity variation, increase or decrease. Required data have been collected from statistical sources of Agricultural Ministry. Results indicated that average Total Factor Productivity growth for corn production of Iran during the specified period is negative (4.5%). It changes in different provinces, as highest value belonged to Fars province whit 18.5% per year and was negative for provinces of Khuzestan, Qazvin, Kerman and Kermanshah.Comparison of each effect shares in order to describing Total Productivity changes showed the highest value of technical efficiency changes.According to significant differences between Productivity growths in various provinces, for each improvement planning of corn Productivity, should consider local conditions and avoid using one program for all of country.

One of the most important objectives of all economic Companies is to get maximum returns of available resources and to achieve high level of Productivity. This study is done in order to measure the Total Factor Productivity in Sari Khosh Noosh Company during 2003-2009 by using monthly data and production function. At first, we took the data of capital stock based on exponential trend. Then, different types of functions were estimated, and finally chose Cobb-Douglas production function as the best form. To guarantee avoiding spurious regression, co-integration tests are used. Ultimately, by using Kenderik index, we calculated the Total Factor Productivity and its growth rate. The results explained that, during of this period, the Total Productivity of the company has decreased.

The study has been done to predict the Total Factor Productivity in Iranian economy in 1996 to 2016 years. In this study, the feed forward neural network whit back propagation algorithm used to predict. 5 different configurations of inputs are used to design 5 models and 30 different scenarios in each model on the number neurons, designed and implemented to predict. Best models of neurons and networks of neurons fourth model with 19 active functions TANSIG input and output function is LOGSIG and by correlation coefficient (R) Mean Square Error (MSE) and root mean square error (RMSE) and normalized root mean square error (RMSE N), a Best model has been selected and they are equal to 0. 9985, 0. 0111, 0. 1055 and 2. 62, respectively. This indicates that the artificial neural networks were designed in this study have the ability to predict the Iranian economy's Total Factor Productivity.