A. Rezaizad, B. Yazdi Samadi, M.r. Ahmadi, H. Zeinali,
Volume 5, Issue 3 (fall 2001)
Abstract
To determine the relationships between yield and its components, and to find the direct and indirect effects of yield-related traits on soybean yield, 240 genotypes were grown in the Research Station, College of Agriculture, Karaj, Iran, in 1997-98 using an augmented design. The study of correlation analysis showed that number of seeds per plant and seed yield per plant had the highest significant correlation coefficient (r=0.92). Other significant correlation coefficients were found between biomass per plant and yield (r=0.86) and between number of pods per plant and yield (r=0.67). Results of stepwise regression analysis revealed that number of seeds per plant, seed weight, and number of seeds per pod were the three major traits affecting seed yield in soybean. However, path analysis showed that only two of the three above-mentioned traits, namely, number of seeds per plant and seed weight, are quite important for soybean selection programs. Stepwise regression analysis was used again, omitting number of seeds per plant, which showed that number of pods per plant, seed weight, plant height and number of days to 90% maturity, are important contributors to yield. Path analysis, this time, revealed that the correlation effects to plant height and number of days to 90% maturity on yield is due to the indirect effects exerted through other traits. It is concluded that three traits, namely, number of seeds per plant, seed weight and number of pods per plant have notable effects on soybean seed yield.
A. Mirzaei, A. Soltani, F. Abbasi, E. Zeinali, Sh. Mirkarimi,
Volume 29, Issue 1 (Spring 2025)
Abstract
Water scarcity and adaptation to it are the most significant issues facing Iran's agriculture. Optimizing the cropping pattern is one of the fundamental strategies for addressing water scarcity. This study evaluated the optimization of the cropping pattern in the irrigated lands of Fars province, one of Iran's key agricultural production areas. Linear mathematical programming and the SAWA system (System for Provincial Agricultural Water Balance and Accounting) were employed. The optimal cropping pattern (OCP) was designed to minimize applied irrigation water and was compared with the current cropping pattern (CCP) as well as a proposed cropping pattern from the Agricultural Jahad Organization (CPAJO) for the province for the cropping year 2023-2024. The results indicated that in the OCP, compared to the CCP, the cultivated area for the following crops decreased: wheat by 30%, barley, grain maize, silage maize, alfalfa, sugar beet, potato, cold- season legumes, and cold-season oil crops by 60%, rice by 80%, warm-season fruit trees by 42%, and vegetables by 13%. Conversely, the cultivated area for warm-season legumes and cold-season fruit trees each increased by 60%, while cold-season legumes increased by 150%. To meet the adaptation goals for water scarcity and sustainable agriculture outlined in this study, a 24% reduction in the irrigated cultivation area of the province was deemed necessary. The OCP achieved a 34% reduction in applied irrigation water at the provincial level without decreasing farmers' income. Compared to the CCP, the OCP led to a 32% reduction in the amount of plant production (by weight) at the provincial level. However, prioritizing plants with higher gross economic profit and lower water consumption over those with lower gross economic profit and higher consumption resulted in the gross economic return of the OCP being comparable to that of the CCP and the CPAJO. The comparison of OCP with CPAJO indicated that the CPAJO has not seriously considered adaptability to water scarcity or agricultural sustainability. The CPAJO needs to be reviewed and optimized to address water scarcity and ensure production stability in light of the impacts of excessive water withdrawal in the province.
Omolbani Mohammadrezapour, Hadi Siasar, Mohammad Javad Zeinali, Mohammad Nazeri Tahroodi,
Volume 30, Issue 2 (summer 2026)
Abstract
Accurate monitoring of water surface dynamics in semi-arid regions poses challenges due to uncertainties regarding the optimal spectral index and sensor selection for effective water resource management. This study assessed the comparative performance of nine spectral water indices across the Landsat-8 and Sentinel-2 platforms to identify the best index-sensor combinations for monitoring semi-arid reservoirs. Utilizing the Google Earth Engine cloud computing platform, 181 satellite images were processed for Golestan Dam in northeastern Iran, comprising 107 Landsat-8 scenes from 2013 to 2023 and 74 Sentinel-2 scenes from 2018 to 2024. After applying atmospheric corrections using the LEDAPS and Sen2Cor algorithms, nine spectral indices (NDWI, MNDWI, ANDWI, AWEI, WI2015, WI1, WI2, LSWI, and NDTI) were calculated and evaluated against the WI2 reference through RMSE, R², and Nash-Sutcliffe efficiency metrics. MNDWI showed superior performance for Sentinel-2 (RMSE=21.42 ha, R²=0.998, NS=0.996), while ANDWI was optimal for Landsat-8 (RMSE=54.54 ha, R²=0.977, NS=0.976). Time-series analysis revealed a 35% reduction in mean annual reservoir area, decreasing from 7.28 km² in 2014 to 4.72 km² in 2021. Consistent seasonal patterns were observed, with spring maxima (9.33 km² in March) and autumn minima (3.35 km² in September) evident across both sensors. A high inter-sensor correlation (r = 0.933) supports the potential for multi-sensor integration in comprehensive monitoring efforts. The LSWI and NDTI indices displayed systematic overestimation due to interference from soil moisture and vegetation, making them unsuitable for quantifying water area. These findings highlight the sensor-dependent nature of optimal index selection, recommending MNDWI-Sentinel-2 pairing for short-term monitoring and ANDWI-Landsat-8 for long-term trend analysis in the management of semi-arid reservoirs.
