Afshin Morovvat; Abdolmajid Ronaghi; Mehdi Zarei; Mostafa Emadi; Mohammad Bagher Heidarianpour; Leila Gholami
Volume 2, Issue 2 , Summer 2012, , Pages 77-82
Abstract
Appropriate management of soil phosphorus (P) fertility in highly calcareous soils of Iran as around the world should rely upon sound knowledge about the phosphorus reserve and its bioavailability. Despite numerous reports on the positive effects of vesicular arbuscular mycorrhizae (VAM) fungi on phosphorus ...
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Appropriate management of soil phosphorus (P) fertility in highly calcareous soils of Iran as around the world should rely upon sound knowledge about the phosphorus reserve and its bioavailability. Despite numerous reports on the positive effects of vesicular arbuscular mycorrhizae (VAM) fungi on phosphorus uptake which is associated to Ectomycorrhiza as a branch of two major branches of group of fungis from mycorrhizal association, surprisingly little data exist on impact of VAM fungi on distribution of soil phosphorus forms in soils. A greenhouse-based study was conducted to investigate the potential effects of Arbuscular Mycorrhizal (AM) fungi application on phosphorus inorganic forms of soil rhizosphere in sunflower plants (Helianthus annuus L.). Results indicated that there is a significant increase in Fe-P fractions (P<0.001) in the rhizospher of the treated sunflowers with AM inoculums compared with untreated sunflowers. It could be potentially attributed to increases in secretion of specific–iron chelates such as hydroxamate siderophore from sunflower roots in +AM sunflowers treatments. [Morovvat et al. Effect of Arbuscular Mycorrhiza fungi application on distribution of phosphorus forms in rhizosphere soils of sunflower (Helianthus annuus L.). International Journal of Agricultural Science, Research and Technology, 2012; 2(2):77-82].
Afshin Morovvat; Mostafa Emadi; Mosa Shojae; Ahmad Pakpour; Leila Gholami; Javad Haji Aghasi; Ehsan Kamali
Volume 2, Issue 1 , Winter 2012, , Pages 23-26
Abstract
Crop yields are dependent on a number of factors such as soil type, weather conditions and farming practices. Crop yield estimates in different soil types are required to meet the needs of farmers, land appraisers, and governmental agencies in Iran as around the world. This study was conducted ...
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Crop yields are dependent on a number of factors such as soil type, weather conditions and farming practices. Crop yield estimates in different soil types are required to meet the needs of farmers, land appraisers, and governmental agencies in Iran as around the world. This study was conducted to model the wheat-grain yields [Triticum aestivum L.] by soil properties in Khoy area, the north-west of Iran. The wheat yields (mean of 5 years) were applied to predict and model the wheat yields under an average level of management used through the area. The prerequisite data on main soil physicochemical characteristics was collected and measured to clarify the correlation and multiple regression analysis which are used to establish the relationships between the soil properties and the wheat-grain yields. Based on the calculated soil index, the general equation (GE) taking the soil index ranging from 0 to 100 % into account was proposed to predict the wheat-grain yields applicably. The results herein markedly proposed other two regression equations for the areas having soil index higher and lower than 70 %, respectively. The results indicated that within three obtained regression models, the equation suggested for the area having soil index higher than 70 % is appreciably more accurate than the model outlined by the FAO and potentially could be recommended for predicting the wheat yield in study area. Moreover, the GE regression model and the proposed model for the area having the soil index lower than 70 % showed the same accuracy compared with the FAO model but calibrated based on the study area condition. Therefore, our proposed regression models for the wheat-grain yields prediction could be used instead of performing the FAO models across the country with approximately same soil and climate status. [Morovvat et al. Wheat yield prediction modeling by soil properties: a case study in North-west of Iran. International Journal of Agricultural Science, Research and Technology, 2012; 2(1):23-26].
