Soil analysis is required for efficient use of inputs viz. seeds, fertilizers, irrigation water and other agricultural planning. However, there are several disadvantages of soil analysis such as they are time consuming, expensive and labour intensive. Many approaches are developed to overcome these difficulties. Hyperspectral spectroscopy is emerging as a promising tool for studying soil, water and vegetation. Therefore, an attempt has been made to review the scope of using hyperspectral reflectance spectroscopy for estimation of soil properties as an alternative to traditional laboratory soil analysis methods. Spectral signature of soil can be used for fast and non destructive estimation of soil properties. Diffuse reflectance in 350-2500 nm range of electromagnetic spectra forms the basis of hyperspectral spectroscopy. An object is characterized by the characteristic absorptions and peaks in the electromagnetic spectra. A number of calibration techniques are applied for establishing relationship between reflectance spectra and soil properties. Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Square Regression (PLSR) are most commonly used techniques. MLR, PCR and PLSR are also used for prediction of several soil properties such as pH, soil organic carbon content, nitrogen, phosphorus, potassium, calcium, magnesium, sodium, iron, manganese, zinc, copper, boron, molybdenum, sand silt, clay and soil moisture. Some commonly used spectral indices are also applied for prediction of soil properties. Some of the soil physical properties viz. sand, silt and clay as well as chemical properties viz. pH and organic carbon could be estimated with good to very good prediction using pure spectra of soil. However, contrasting results of prediction of soil properties using multivariate analysis techniques have also been reported. The content of this review article will be helpful for researchers who are working on alternate methods of estimation of soil properties.
Understanding of spatial distribution of available soil nutrients is important for sustainable land management. An attempt has been made to assess the spatial distribution of available soil nutrients under different soil orders and land uses of RiBhoi, Meghalaya, India using geo-statistical techniques. Seven Land Use Land Cover (LULC) classes were selected from LULC map on 1:50,000 scale prepared by National Remote Sensing Centre (NRSC) viz. Abandoned Jhum (AJ), Current Jhum (CJ), Deciduous Forest (DF), Double Crop (DC), Evergreen Forest (EF), Kharif Crop (KC) and Wastelands (WL). Again, three soil orders were identified by National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) in RiBhoi district of Meghalaya, India viz. Alfisols, Inceptisols and Ultisols. 105 soil samples were collected, 5 replicated soil samples from 21 strata derived from 7 LULC and 3 soil orders. Soil samples were analyzed for available nitrogen (N), available phosphorus (P2O5), available potassium (K2O) and available zinc (Zn) using standard procedures. One way ANOVA was carried out using IBM SPSS Statistics 20.0 software. Significance levels were tested at p≤0.05. N content varied from low (215.50 kg/ha) to medium (414.30 kg/ha) with mean value of 291.50 kg/ha. On the other hand, P2O5 content varied from low (19.90 kg/ha) to high (68.30 kg/ha) with mean value of 43.52 kg/ha. Similarly, K2O content varied from low (112.09 kg/ha) to high (567.84 kg/ha) with mean value of 273.68 kg/ha. Again, Zn also varied from low (0.26 ppm) to high (1.46 ppm) with mean value of 0.64 ppm. In Alfisols, N was found to be higher in EF, AJ & CJ than DF, DC, KC and WL. KC has been found to have lower N than all other LULC classes. Higher P2O5 has been found under EF over KC and WL. AJ has been found to have higher K2O than all other LULC classes. K2O has also been found to be higher in CJ over DC, KC and WL. DF and EF have been found to have higher K2O than KC and WL. Zn has been found to be higher in EF over CJ, DC and WL. In Inceptisols, higher amount of N was observed under EF over all other LULC classes. Higher N has also been found under CJ over DF, DC, KC and WL. P2O5 content was found to be higher under DF over all other LULC classes. Higher P2O5 content was also found under AJ, CJ and DC than KC and WL. Higher amount of K2O has been found under AJ over all other LULC. K2O content of soil under DF was also higher than CJ, EF, KC and WL. Zn has been found to be higher under EF over all other LULC classes. Zn content under CJ has also been found to be higher than AJ, DF, KC and WL. In Ultsols, higher amount of N has been found under EF compared to all other LULC classes. Lowest N content was found under KC. P2O5 content was found to be higher under EF, DF and AJ over all other LULC. K2O content has been found to be higher under CJ in comparison to all other LULC classes. K2O content of EF and DF were also found to be higher than AJ, DC, KC and WL. Again, K2O content has been found to be higher under DC compared to AJ, KC and WL. Zn content under EF and AJ was found to be higher than all other LULC classes. CJ, DF, DC, KC and WL have been found to have lower Zn content. It has been observed that P2O5 content is significantly higher in inceptisols irrespective of LULC classes. The study has highlighted the spatial distribution of available soil nutrients as a function of soil orders and LULC. This will be a useful input in sustainable land management programmes.
The topological settings of the 10 locations of adjacent paddy field near coal mine belt of East Jaintia Hills significantly influenced in causing extreme soil acidity and affect the bio-availability of soil nutrients. It was found that two categories of acidities such as extreme acidity (mean pH 3.16) and moderately acidity (mean pH 4.22) were generated. Results indicate decreased of acidity along the topological settings from top to toe with affirmative relationship with Ex. acidity (0.37 to 1.19 meq/100g), Ex. aluminium (2.86 to 4.35 meq/100g), change in lime requirement (13.75 to 28.67 t/ha) and slight changes in effective CEC (ECEC). Soil contain high organic carbon (SOC, 2.08-2.43%), available nitrogen (N, 293.35 to 319.71 kg/ha), sulphur (S,21.01 to 30.98 kg/ha) and iron (Fe, 222.17 to 241.78 ppm), but low available phosphorus (P 2 O 5 , 14.36 to 19.31 kg/ha), DTPA extractable zinc (Zn, 0.27 to 0.44 ppm) and microbial activities. Observations reveals that soils in low laying topographical settings of the paddy field were found maximum in almost all the parameters in comparison with other topographical settings of the study area.
To study the effect of organic and inorganic nutrients application in vegetable pea in vegetable pea-maize cropping sequence, a two years field experiment was conducted on the experimental farm of the College of Post Graduate Studies (CAU-I), Umiam, Ri-Bhoi (Meghalaya) during 2014-15 and 2015-16. The treatments included three organic nutrient sources viz., FYM (5 t ha-1) (B1), Rhizobium + phosphorus solubilizing bacteria (PSB) (B2) and Rhizobium + PSB + FYM (5 t ha-1) (B3), and six inorganic nutrient sources viz., RDF (F1), RDF + Lime (0.5 t ha-1) (F2), 75 % RDF (F3), 75 % RDF + Lime (0.5 t ha-1) (F4), 50 % RDF (F5) and 50 % RDF + Lime (0.5 t ha-1) (F6) were replicated thrice in randomized block design. Among organic nutrient sources, treatment B3 recorded maximum values of growth, yield attributes, yields and economic returns which were high over B1 and B2 in both the years however, plant height, pod length, number of grains pod-1, seed index, harvest index, net return and B:C ratio in both the years. Similarly, gross return, net return, and B:C ratio did not differ significantly due to organic nutrients application in pea in both the years except for gross return in the second year B3 recorded significantly high gross return over B1 and B2 organic sources application in pea. Among inorganic nutrient sources, maximum values of growth, yield attributes, yields and economic returns were observed from F2 treatments those were significantly high over the same recorded from remaining inorganic nutrient treatments to vegetable pea in both the years except for seed index in the first year and stover yield and harvest index in both the years.
A study was carried out in Ri-Bhoi district of Meghalaya. The studies have focused on organic carbon (C) stocks of soils because of increases in atmospheric carbon dioxide (CO 2). Six agro-ecological land use were intersect with 7 slopes to generate soil sample location. Three locations have been selected for each strata to determine the average carbon stock. The mean organic soil organic carbon ranged between 1.53 per cent to 2.43 per cent with maximum in S1T2 (2.42%) followed by S2T2 (2.38%), S2AG2 (2.20%) and minimum in S7OS1 (1.53%) followed by S7OS2 (1.56%) and S6OS1 (1.60%). The mean BD ranged between 1.22 to 1.42 g/cm 3 with maximum in S7OS2 (1.42 g/cm 3) and minimum in S1T2 (1.22 g/cm-3) and S3T2 (1.22 g/cm-3). The mean SOC stock ranges from 44.41 Mg ha-1 to 32.5 Mg ha-1 with mean SOC stock content was found maximum in S1T2 (44.41 Mg ha-1) followed by S2T2 (43.49 Mg ha-1) and minimum in S7OS1 (32.51 Mg ha-1) followed by S7OS2 (33.1 Mg ha-1).
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