Articles
Publication Date: 2024
Asian Journal of Technology Innovation (19761597)33pp. 805-838
Technological innovation systems have mostly been studied in cities and focused on specific technologies. As the villages are comparatively different in scale and socioeconomic and technical characteristics, the functional expectations of technology in rural environments are also different. In this regard, this study aims to identify the functions of the Rural Technological Innovation System (RTIS) in irrigation systems of rural areas in Taft County, central Iran. Applying the Grounded Theory Method, the research is conducted qualitatively. Data was gathered primarily, by interviewing the managers and experts of water, agriculture, and technology. Results identified 12 functions for RTIS, the Seven are overlapped with the functions previously proposed for other TISs. New distinct ones are ‘guaranteeing spatio-social justice’, ‘continuous monitoring and choosing appropriate technology’, ‘accountability and transparency’, ‘Positive imagery and raising hope’ and ‘ensuring sustainability and enhancing effectiveness’. Identifying the functions an RTIS should have, revealed some deficiencies the studied villages face in developing innovation and technology that are expected to be resolved by implementing an RTIS. © ASIALICS, STEPI 2024.
Publication Date: 2019
Scientia Horticulturae (3044238)246pp. 528-534
Soil properties and crop yields are strongly interrelated. Identifying the relation can leads to better management of cultivation and land suitability evaluation. Some difficulties related to statistically analyzing the soil properties, especially in relation with the crops and water, arises from the fact of their high variability and intercorrelation, which causes the multicollinearity problems. Adequate choice and use of multivariate statistical methods is appropriate to approach these areas of investigation. This study deals with the relations and interdependencies of soil physio-chemical attributes and pistachio (Pistacia vera L.) yield, as the most important commercial nut crop of Iran and one of the most worldwide. The study area is the pistachio orchard farms of Anar region in Rafsanjan County, Kerman, Iran; and the employed canonical multivariate methods are redundancy analysis (RDA) and discriminant analysis (DA). Six orchards were chosen with the same managerial procedures, pistachio cultivars (Owhadi) and tree ages. But, due to the aim of distinguishing influential soil properties, the vegetative growth and pistachio yields were different. Each orchard then was divided to two suitable and unsuitable parts, based on the actually measured yields. In each part, three replicates of soil sampling (in to depths of 0–50 and 50–100 cm) and three trees for eachever were considered in order to determining the pistachio yields. To explore the interrelationships between soil properties, orchard suitable/unsuitable parts and yields and distinguishing misclassified orchards based on the yield parameter and vegetative growth, the canonical multivariate RDA was employed. Results showed a relatively strong correspondence between yield and soil properties. Clay, EC, K and B were negatively related and sand and CaCO3 significantly positive correlated and altogether explained 100% of yield total variation. The multivariate DA then adopted to reclassify the orchard samples into two performance groups based on the variations of soil attributes. Results indicated two influential variables of clay and EC in distinguishing land suitability for pistachio farming, were able to classify 80.5% of orchards correctly (73.7% to suitable orchards and 88.2% to unsuitable ones). Overally, results showed a significant difference between soil properties in suitable and unsuitable areas, as well as a significant relationship between some soil properties and the yield of pistachio. Findings, applicably recommend that site suitability for optimal pistachio cultivation can satisfactorily be evaluated using only few and easy-determining soil properties, throughout canonical discriminant functions of appropriate multivariate analysis. © 2018 Elsevier B.V.
