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
N. Asgari; S. Ayoubi; A. Dematte; H. Khademi
Abstract
Introduction Carbonates are an essential and prominent constituent of soil chemical properties particularly in arid and semiarid regions, in regards with soil productivity and conservation. The conventional techniques for assessing soil properties rely on direct laboratory measurements which are expensive, ...
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Introduction Carbonates are an essential and prominent constituent of soil chemical properties particularly in arid and semiarid regions, in regards with soil productivity and conservation. The conventional techniques for assessing soil properties rely on direct laboratory measurements which are expensive, time consuming and labor intensive. Hence, it is required to develop fast and cost-efficient techniques for evaluation of mentioned parameters. The Koppen climatic classification generally categorizes Iran among the arid and semi-arid climates. About 90 % of its lands are arid or semiarid. According to Soil Survey Staff (2014), calcareous soils contain 5% or more volumes of inorganic carbon (or carbonate calcium equivalent), which are the prevailing formation in arid and semi-arid areas. These soils are typical of areas where minerals cannot be leached away from the soil profile due to low precipitation. Based on the reports of FAO.UNDP (1972), approximately 12% of soils all over the world and 65% in Iran are calcareous. Therefore, carbonate is a key component that physically and chemically influences soil properties, as well as its fertility and productivity. One of the fast, easy-to-use, cost-effective and non-destructive methods of soil analysis is the visible to near-infrared (Vis-NIR) and mid-infrared (mid-IR) spectroscopy, that can partly be employed for the optimization of traditional techniques. Therefore, the reflectance spectroscopy is considered as one of relatively inexpensive and fast techniques to evaluate these features. The purpose of the present study was to evaluate the capability of the reflectance spectroscopy technique in Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges to estimate soil carbonates content as one of the key components affecting the physical and chemical properties of soils (especially in arid and semi-arid regions). Materials and Methods The study area is located in Juneqan District, Chaharmohal and Bakhtiari Province, southwest of Iran. 272 soil samples were collected from a depth of 0-10 cm, air dried and passed through a 2 mm sieve. The carbonates value of each sample was determined by standard laboratory method. The spectral reflectance of soil samples was extracted in the Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges using a spectroradiometer FieldSpec 3 (ASD-Analytical Spectral Devices, Boulder Colorado, USA) and Nicolet 6700 Fourier Transform Infrared (FT-IR) (Thermo Fisher Scientific Inc., Waltham, MA), respectively. In the next step, seven preprocessing methods included absorbance transformation (log [1/reflectance]) (Abs), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), Savitzsky-Golay derivation (SGD), Continuum removal transformation (CR), Normalization in range <-1,>1 (Nor) and Detrend (Det), were performed over original spectra for correcting light scattering in reflectance measurements and data improvement before using data in calibration models. Afterward, The dataset (272 samples) for each spectra range was randomly divided in calibration (70%) and validation (30%) datasets. Four different calibration models were fitted over Vis-NIR and mid-IR spectra to develop carbonates prediction models including: Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), Random Forest (RF) and Gaussian Process Regression (GPR). The evaluation of soil predicting models was done according to the value of R2, RMSE and RPD. According to some researches, RPD values more than 2 shows that the models provide precise predictions, values of RPD between 1.4 and 2 are considered to be reasonably representative, and values less than 1.4 indicate poor predictive value. Results and Discussion The carbonates content in studied samples ranged from 1 to 76% with an average value of 24.7%. Overall, carbonates content promoted increase of spectral reflectance intensity on several region of spectrum in both spectral ranges. The specific absorption wavelength in Vis-NIR spectra used to indicate the presence of soil carbonates was 2338 nm and in the mid-IR range were 714, 850, 870, 1796, and 2510 cm1. The results showed that the best performance of the used models in the Vis-NIR spectral range was related to the SVM model (R2=0.81, RMSE=5.36) and in the mid-IR range allocated to PLSR model (R2=0.86, RMSE=4.5). Both of these models showed great accuracy in carbonates estimating (RPD>2). Besides, the results showed that the mid-IR spectral range in the prediction of carbonates provided better performance than the Vis-NIR range. This can explained by the fact that the fundamental molecular vibrations of soil components occur in the mid-IR range, while only their overtones and combinations are detected in the Vis-NIR range. Conclusion It seems that the reflectance spectroscopy technique can be considered as a precise substitute for the conventional methods of measuring carbonates, which are sometimes costly, time consuming and destructive. However, due to the spatial and temporal variability of soil properties as well as the huge variety of models and spectral preprocessing methods, it is necessary to examine the capability of this technique in other areas with other preprocessing methods and regression models.