Investigating the Affecting Components of Water Productivity by Factor Analysis Approach (Case Study: Qazvin Province)

Background and Objectives
Many countries around the world are currently facing a major challenge of producing food from limited water resources. Water scarcity, coupled with the misuse of valuable water resources, does not meet the current growing demand for food by the population. Therefore, in the current situation and in order to alleviate poverty and hunger, the most important challenge for the agricultural sector is the strategy of using water more efficiently. In the long run, increasing water productivity to expand irrigated lands and increasing rainfall productivity to produce more food is a key factor in combating hunger and reducing poverty (Kiani and Sedaghat Doost, 2016). Water scarcity in Iran is also an undeniable climatic reality. The total volume of water resources by rainfall is 403 billion cubic meters in the country, of which a significant amount becomes unavailable in the form of evaporation. The volume of renewable water resources is about 100 billion cubic meters, which is a water consumptionin the agricultural sector of about 80% (Naseri et al., 2017). Moreover, the rapid increase in population along with the expansion of urbanization has increased water consumption; whaich has led to the consumption of about 69% of the country's total renewable water that is very high compared to other countries. Meanwhile, agriculture in Iran is in dire need of water. According to the studies, identifying the factors affecting water productivity is of great importance due to the existing conditions (water scarcity) and factor analysis can be used in this regard. Therefore, the present study was planned and conducted to investigate the factors acting water productivity by means of factor analysis.
 
Methodology
The study area is located in the central latitude and longitude of Qazvin province between 48 degrees and 44 minutes to 50 degrees and 51 minutes in the east of Greenwich meridian and 35 degrees and 24 minutes to 36 degrees and 48 minutes in the north latitude relative to the equator. The total area under cultivation of crops and horticulture in Qazvin province is 334.7 thousand hectares on average, of which about 2323.4 thousand hectares (equivalent to 66.5%) are irrigated and about 1112.3 thousand hectares are (equivalent to 33.5%) rainfed (Anonymous 2019a, Anonymous 2019b). In this study, three methods including documentary and library study, searching through electronic sources, and field study have been used to collect the required information. The data collection tool was a questionnaire that consists of two parts: personal profile and variables affecting water productivity. The sampling method in this study was completely random. In this study 317 questionnaires were completed. The percentage of variance and eigenvalues of different factors were used to determine the maximum effect of factors in explaining the variance of the data.
Findings
Examining the age and education level of farmers showed that farmers are more in the age range of 40- 61 years and older and their education is mostly Middle School Diploma and lower. The first factor among the nine extraction factors, with a specific value of 4.55, explained 14.67% of the variance of the whole analyzed set. Then the second factor, with a specific value of 3.84, was able to explained 12.39% of the variance of the set. The third factor, with a specific value of 3.22, explained 10.39% of the variance of the whole set under analysis. The fourth, fifth, sixth, seventh, eighth, and ninth factors, with special values ​​of 2.56, 1.65, 1.60, 1.50, 1.40, and 1.16, explained about 8.25, 31. 5, 16.5, 4.84, 4.50, and 3.74 the total variances, respectively. Overall, the nine extracted factors were able to explain about 69.25% of the total variance, which indicates the appropriate variance explained by the extracted factors. Among the nine factors obtained in this study, the components of agricultural-managerial measures and technical-infrastructure measures were recognized as the most important ones. The former factor explained 14.67% of the variance of the whole set under analysis and the lattar factor was able to explain 12.39% of the variance of the set.
Conclusion
In this research, nine extracted factors were included in the order of "agricultural-managerial measures", "technical-infrastructural measures", "irrigation management", "extension factors", "fertilizer recommendations", "field time management", "mechanization", "farmer's education level" and "principled control of pests and weeds". The results of this study can be used by managers and researchers in planning to improve water productivity in the agricultural sector.