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A well-designed Agricultural Extension System (AES) can facilitate transitions towards sustainable agriculture. However, in most developing countries, AES has failed to promote sustainable agricultural development. The New Agricultural Extension System (NAES) was initiated to facilitate agricultural development in Iran. However, there is still no definite reflection on the influences of NAES on agricultural development. Therefore, this research aimed to evaluate the performance of NAES. To ensure an integrative and holistic analysis of the NAES's performance, the Complexity Theory (CT) and Agricultural Innovation System (AIS) approaches were used. To gather data, survey research was conducted in Fars Province, southern Iran. A multi-stage random sampling was used to identify the designated extension agents. The findings indicated that effective implementation of the NAES needs facilitated interaction through network mediation (X = 71.6), creation of an enabling context for the delivery of extension services (X = 66.6), improved communication and marketing infrastructures (X = 72), development of a value chain (X = 71.4), acceptance of self-organization (X = 67.8) and adoption of complexity-aware management (X = 66). The results also identified the hindering effects of demographic, structural and psychological factors on the practical application of CT and AIS principles. Some recommendations and implications are offered to improve the effectiveness of NAES. [ABSTRACT FROM AUTHOR]

Agriculture is an essential instrument for development, poverty alleviation, and food security in developing countries, such as Iran. The agricultural sector of Iran is dominated by small-scale farming, and the yield of most crops is low. A well-designed and well-functioning agricultural extension system can facilitate the transition toward productive, profitable, and sustainable agriculture. However, the top- down agricultural extension system of Iran has left most smallholder and resource-poor farmers with no access to advisory services, and their problems have remained unresolved. The new agricultural extension system (NAES) was initiated in 2014 to better respond to the problems of smallholder farmers. However, there is still no definite reflection on the influences of the NAES on agricultural development. Therefore, this study aimed to evaluate the performance of the NAES. Also, it purposed to identify the blocking mechanisms hindering the NAES’ function. In this regard, qualitative research was planned and conducted in Fars Province, Southern Iran. The findings revealed that NAES has managed to enhance the availability of extension services for smallholder farmers, improve the productivity and profitability of agriculture, and reduce livelihood vulnerability. However, it has failed in facilitating access to remunerative markets, mobilizing sustainable financial and human resources, and being entirely demand-driven because of financial, physical, institutional, market, natural, informational, capability, and psychological constraints. Some recommendations and implications are offered to improve the effectiveness of the NAES. [ABSTRACT FROM AUTHOR]

Morphological features of Golden mullet (Liza aurata) population in the southern coasts of the Caspian Sea were studied. A total of 228 fish specimens were collected from Bandar Anzali, Kiashahr, Babolsar and Bandar Turkman in Iranian waters. A combination of Truss network, traditional system and few meristic counts were simultaneously applied to the sample set. Statistically high significant morphometric differences were observed between the four L. aurata stocks. Univariate analysis of variance (ANOVA) revealed significant differences between the means of samples for 10 out of 25 standardized morphometric measurements of truss system, 3 out of 7 measurements of traditional morphometric system and also the number of second dorsal fin spines, soft rays of second dorsal fin, total number of pectoral fin rays, soft rays of anal fin, number of squmosum scales and pyloric caeca among meristic counts. Discriminant analysis correctly classified 76% and 70.3% of individuals into their origina.....