K. Kamali; Gh. Zehtabian; tayybe Mesbahzadeh; M. Arabkhedri; Hossain Shohab Arkhazloo; A. Moghadamnia
Abstract
Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces ...
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Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces study time, decreases monitoring cost for sustainable use of agricultural lands. The aim of this study was to introduce the most effective soil characteristics of agricultural lands in Mohammadshahr plain, Karaj, to prevent the descending trend of soil quality.
Materials and Methods: In this study, four farms and orchards which were different in terms of crop type and irrigation system were selected and evaluated with Integrated Quality Index (IQI) and Nemero Quality Index (NQI). In both indicators, the characteristics affecting soil quality are combined in the form of a mathematical model and presented as a numerical quantity. For this purpose, first 12 soil profiles were described, followed by sampling from topsoil (surface layer) and sublayers (weighting average for the depths) and testing 17 soil characteristics affecting its quality. In the next step, both indicators were calculated using two different sets of soil properties. The first category, the Total Data Set (TDS), included all measured soil characteristics, and the second group, the Minimum Data Set (MDS), included the most important properties affecting soil quality. The Principle Component Analysis was implemented to select the MDS. Soil properties were scored to calculate IQI and NQI. For this purpose, a function was defined for each soil feature to standardize all scores between zero and one. Weighting various soil quality properties was also performed by calculating the common variance of the variables, which was obtained by factor analysis method.
Results and Discussion: Calculation of IQI and NQI indices showed that the topsoil samples were in grade III and sublayer samples belonged to grade IV with major limitations due to lack of profile development, organic carbon deficiency, salinity and high gravel. Four and six items out of 16 variables were identified effective for topsoil and sublayers, respectively. The IQI index based on TDS was more accurate and sensitive than the NQI index for soil quality assessment, as more features are considered for TDS. In the IQI index, both the weight of attributes and their scores are effective, while in the NQI index, only the attribute score is considered. On the other hand, the coefficient of determination between the TDS and MDS for topsoil and sublayer samples was 0.55 and 0.56% for IQI model, respectively, and 0.48 and 0.16% for NQI model, respectively. In other words, the determination coefficients showed the reliability of using the MDS instead of TDS in both IQI and NQI models. In the MDS, mean weight diameter (MWD) showed the highest effect on the surface layer and percentage of gravel had the greatest impact on the soil quality of the sublayer.
Conclusion: Although TDS took into account all soil properties and showed a slightly higher coefficient of determinations with both soil quality indicators, the MDS obtained similar results to the TDS with only about half of the properties. In the MDS, the features with an internal correlation is eliminated rendering it more cost effective. The results of this study assist decision-makers to choose better quality management and soil sustainability strategies while decreasing the monitoring cost.
B. Hassanpour; F. Mirzaei; S. Arshad; H. Kossari
Abstract
In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL ...
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In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL and S-SEBI algorithms. These algorithms are different in predicting sensible heat flux. The results show that RSME value for the net radiation and soil heat flux was respectively 46 and 43 (w/m2). SEBAL algorithm is capable to estimate sensible heat flux more accurate than S-SEBI so it is able to estimate latent heat flux more accurate. The RSME amount in sensible heat flux and latent heat flux for SEBAL algorithm are 58 and 31 (w/m2) respectively. These amounts in S-SEBI algorithm are 111 and 74 (w/m2). The differences between two algorithms could be because of the use of meteorological data in predicting sensible heat flux and aerodynamic resistance in SEBAL algorithm. Also the results show that SEBAL algorithm estimates hourly evapotranspiration by the difference of 0.05 mm/hour which is about 1% of hourly evapotranspiration Whereas S-SEBI predicted it by the difference of 0.11 and 11%. The difference between measured daily evapotranspiration and SEBAL -based daily evapotranspiration was 0.4 mm that is about 1% less than measured. Whereas these differences by S_SEBI are 1mm and 12%.
