Soil, Water and Plant Relationships
Anahita Hadighanavat; Abdolrahim hooshmand; Parvaneh Tishehzan; Naser Alemzadeh ansari,; Kazem Rangzan
Abstract
Introduction: Nowadays, the management of water resources has become one of the major challenges in the world due to recent droughts and water shortages. Therefore, timely and non-destructive monitoring of Water use efficiency (WUE) and yield of plants to screen cultivars with high water use performance ...
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Introduction: Nowadays, the management of water resources has become one of the major challenges in the world due to recent droughts and water shortages. Therefore, timely and non-destructive monitoring of Water use efficiency (WUE) and yield of plants to screen cultivars with high water use performance and efficiency and rational allocation of water has become one of the important goals in agriculture. Compared with traditional crop Yield and WUE monitoring and diagnostic tools, hyperspectral remote sensing technology has made it possible to obtain water use efficiency and Yield by taking advantage of large amounts of continuous data on a large scale. Therefore, this study aimed to create a model capable of estimating the WUE and Yield of tomato plants based on hyperspectral remote sensing data by establishing a relationship between common spectral indices and WUE and Yield in greenhouse conditions.Materials and Methods: The research was carried out during the growing season of 2021 from January to June at the research greenhouse of the Department of Water and Environmental Engineering, Shahid Chamran University, Ahvaz, Khuzestan Province, The main factor was irrigation treatments in three levels including full irrigation, 20%, and 40% deficit irrigation, and the sub-main factor was silica nanoparticles with concentrations of 0 and 100 mg/lit. The pot experiment plot was laid out in a split plot in a randomized complete design (RCD) with four replications (120 pots in total). Then Tomato yield and WUE under the various treatments, were calculated. Canopy hyperspectral reflectance was measured using a portable spectrometer (ASD FieldSpec 3) operated in the spectral range of 350-2500 nm. The spectral data acquisition was conducted in four stages of plant growth during the growing season and the data were used to calculate spectral indices NWI5, WI3, WI4, NDVI, NDVI, and OSAVI. Then the ability of spectral indices to evaluate the water use efficiency and yield of tomato plants in different irrigation regimes and nanoparticles was investigated. Analysis of variance (ANOVA) was performed on spectral indices and WUE and yield of tomato plants using a split plot design. The PROC GLM method of SAS software (version 9.4, SAS Institute, Inc., Cary, NC, USA) was used for this analysis. Then, in order to compare the averages and whether they have a significant difference with each other at the 0.01 and 0.05 levels, the least significant difference (LSD) test was used.Results and Discussion: The results showed that the Water use efficiency (WUE) under deficit irrigation treatments is increased with increasing water stress but the yield of tomato decreases with increase of water stress. In addition, the WUE and yield of tomato increases with increasing the concentration of silica nanoparticles (from 0 to 100 mg/liter). The value of NDVI and OSAVI indices decreased with the increase of water stress, while the value of RDVI, WI3, WI4 and NWI5 indices increased. The amount of NDVI and OSAVI spectral indices in the treatment containing 100 mg/liter nanoparticles was higher than the treatment without nanoparticles. Also, the amount of spectral indices RDVI, WI3, WI4 and NWI5 in nanoparticles with a concentration of 100 mg/liter was lower than the control treatment (zero concentration). The results also showed that the coefficient of determination between the different spectral indices and the WUE and Yield index was 0.55**Furthermore, among the six spectral indices, three spectral indices (NWI5, WI3, and WI4) jointly met most of the criteria used to determine the accuracy of the models for predicting yield and WUEConclusion: Maintaining existing water resources through improving irrigation management and increasing water use efficiency and Yield of plants is the main goal for the sustainable development of water agriculture. As a result, rapid and non-destructive monitoring of water use efficiency and Yield is of great importance in improving irrigation management of crops and saving water consumption. significant relationship with yield and WUE index of tomato plants in greenhouse conditions. In conclusion, Spectral indices studied in this research could be useful and non-destructive assessments of the water use efficiency and yield of tomato in greenhouse conditions.Keywords: Deficit Irrigation, Spectral indices, spectroscopy, Tomato Plant, Water use efficiency
Soil Genesis and Classification
Fatemeh Rahmati; ُSaeid Hojati; Kazem Rngzan; Ahmad Landi
Abstract
Introduction: Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance ...
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Introduction: Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. Accurate mapping of SOC’s spatial distribution is a key assumption for soil resource management and land use planning. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The digital environment needs to have soil continuous maps at local and regional scales. However, such information is always not available at the required scale. Therefore, DSM approach is a key solution for quantifying and assessing the variation of soil properties such as SOC using remotely sensed indices and digital elevation model (DEM) as the most commonly useful ancillary data for soil organic carbon prediction. In this way, the data mining techniques is the pathway to create digital soil maps. Therefore, this study was carried out to compare the two common machine learning algorithms including random forest and multiple linear regressions in digital mapping of surface SOC in the Semirom County, Isfahan province. The digital maps of SOC using the two above-mentioned algorithms were also created and the most important variables affecting the distribution of SOC in the study area reported. Materials and Methods: A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. Based on the synoptic meteorological station reports, the annual average temperature was in the range of 7.5-12.5 ▫C, the annual precipitation ranged between 350-450 mm. Soil moisture and temperature regimes are Xeric and Mesic, respectively. Then, using the Global Positioning System (GPS), sampling was done from the soil surface layer (0-10 cm). The preparation of soil samples includes air drying, pounding and softening of the collected samples performed, and then the samples were passed through a 2 mm sieve. Then, the amount of organic carbon in the samples was determined utilizing the Walkley-Black method. Also, in order to evaluate the effect of other soil properties on the organic contents of the soils, laboratory analyzes including saturated soil moisture content, soil texture, soil pH in saturated pastes, electrical conductivity of the soil saturation extracts and the calcium carbonate equivalent of the soils were measured utilizing standard laboratory protocols.In this research, auxiliary variables including terrain parameters and vegetation indices were derived from digital elevation model (DEM) and the Landsat 8 OLI satellite images employing ArcMap version 10.4.10 and SAGAGIS version 6.0.4. Then, all auxiliary layers were converted to raster format using the “raster” package and merged with each other using the “Covstack” function. Afterwards, the values of the all environmental covariates at each sampling point were extracted in a single file using the “extract” function of the “sp” package in the RStudio environment. Then, using SPSS software v.19 and the principal component analysis (PCA) method, among the 29 auxiliary variables used in this research, the most important auxiliary covariates were used in the modeling process. The dataset were then split into two groups referred to as calibration (80%) and validation (20%) subsets. Finally, SOC contents of the soils were predicted and mapped using multiple linear regression (MLR) and random forest (RF) algorithms in RStudio environment. MLR and RF algorithms were run employing “lm” and “randomForest” packages, respectively. Five different statistics was used for evaluating the performance of each model including the coefficient of determination (R2), bias, root mean square error (RMSE), nRMSE, and mean bias error (MBE). Results and Discussion Based on the descriptive analysis of the soil samples, soils of the study area were characterized as non-saline, alkaline, and calcareous soils. The SOC contents of the soils ranged from 0.3 % to 2.2% with the mean value of 0.89 %. The coefficient of variation for the SOC contents was 21.7%, based on which soils of the study area are classified as soils with the moderate variability considering the values proposed by Wilding (1985). The results of PCA showed that the most important auxiliary variables could be used for the modeling process are slope aspect, channels network base level, catchment slope, total curvature, height, longitudinal curvature, mass balance index, modified catchment area, slope degree, slope length, topographic position index, vertical distance to channel network, soil adjusted vegetation index, transformed vegetation index, difference vegetation index, ratio vegetation index, and general curvature.These variables explained 80% of the total variance over the study area. The comparison of the two different SOC prediction models, demonstrated that the RF model (ntree =1000 and mtry =10) with the R2, RMSE, nRMSE, and bias values of 0.79, 0.12, 0.13, and 0.002 respectively, had a better performance rather than MLR model in this study. The first five very important variables detected by RF algorithm to predict SOC contents over the study area were transformed vegetation index, ration vegetation index, soil adjusted vegetation index, and slope degree. The final map of the surface SOC distribution over the study area shows that although the estimates made by the RF algorithm have provided better estimates compared with the MLR model, but caused overestimation and/or underestimation in predicting the minimum and maximum values of the surface SOC contents, respectively. Conclusion The results of this study showed the better performance of the RF regression algorithm due to its ability to take into account the nonlinear and complex relationships between SOC contents and the environmental covariates compared to the MLR method.