Assessment of nutrient status and develop its blueprints using geo-statistical approach in soils of dindori district

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dc.contributor.advisorSharma, B.L.
dc.contributor.authorJangid, Vandana
dc.date.accessioned2017-02-27T06:10:03Z
dc.date.available2017-02-27T06:10:03Z
dc.date.issued2016
dc.descriptionSoil is one of the most important non-renewable natural resource and soil fertility is its crucial component for increasing agricultural production. Now it is being increasingly emphasized that, for sustaining soil health. We need spatial information on soils along with its fertility levels. Towards this end, GIS plays a very effective role, especially geo-statistical techniques. Continuously increasing population pressure on the earth’s ecosystem to produce food and fiber will place greater demand on soils to supply essential nutrients. In India, continuous cropping for enhanced yield removes substantial amounts of nutrients from soil. Macronutrients; as they are required in large quantities for growth, increase plant productivity, leaf and grain yield. However, the availability of nutrients in soils differ widely and depending upon the geophysical constraints like nature of the rock; climate and topography, soil pH and the susceptibility of the soil to compaction are dependent on the constituents of the original parent rock. Similarly micronutrients Zn and Fe deficiency can cause nutritional imbalance in the soils which may results in significant reduction in the productivity of the crops. On the other hand, residue burning, removal and burning of crop residues and cow dung is used mainly for firewood detrimental to soil fertility and thus productivity, as these results in the reduction of soil organic matter and loss of other soil nutrients. Consequently, increasing crop production on these soils is only possible with appropriate nutrient management practices. In classic statistic method, needed extensive sampling to decline variation correlation. Despite high costs spent in this method, the results are limited only on mean value for specific classes. Generally, in most of the soil nutrient studies, more emphasis was given to macronutrients; but studies in the micronutrients status of soils are scarce. Some studies were undertaken but latitude, longitude of sampling sites is missing many times. Keeping above in view, the present investigation entitled “Assessment of nutrient status and develop its blueprints using Geo-statistical approach in soils of Dindori district” was carried out under AICRP on MSN at Department of Soil Science and Agricultural Chemistry, College of Agriculture, JNKVV, Jabalpur (M.P) during 2015-16. Geographically, Dindori district lies in between 220 17’ to 230 22’ North latitude and 800 35’ to 800 58’ East longitude with an area of 6128 km2. The district is divided into seven blocks i.e. Dindori, Shahpura, Mehadwani, Amarpur, Bajag, Karanjiya, and Samnapur. The sites distributed over agricultural areas by considering land use and heterogeneity of the soil types. GPS based three hundred fifteen surface soil samples (0-15 cm) and forty eight plant samples were collected from farmer’s field during 2015 off season. These soil samples were analyzed for physico-chemical properties and total and available macro and micronutrients. In addition, the nutrient index (NI) values for available nutrients present in the soils were also calculated. The macro and micronutrients in plant were determined using standard methods. After data arrangement, blueprints of macro and micronutrients were generated using geo-statistical tool (GIS 9.3.1 software). Correlations study between variables and PCA was done to reduce the dimensionality of a data set carried out using SPSS 16.0 software. Result obtained from present study using appropriate methodology, summarized below: The soils under the study were slightly acidic to neutral in reaction, safe in electrical conductivity, low to medium in organic carbon and slightly calcareous in nature. Further, the status of soils with respect to available macro and micronutrients has been drawn in terms of nutrient indexing. The available N, P and K status was low, medium and medium (L-M-M) with nutrient index value of 1.37, 1.99 and 2.32, respectively. The available S was low to high in whole district. Result revealed that individually N (63.17%), P (32.70%), S (54.60%) and Zn deficiency (41.59%) was widespread and Fe deficiency (3.17%) was emerging while copper, manganese and boron were sufficient in district. Result showed that the available N content was also low in the order of S6> S4> S8> S5> S11> S14> S12> S9> S2> S13> S3> S7> S10> S1> S15. The available P content was in the order of S4> S10> S2> S6> S12> S8> S13> S7> S14> S1> S9> S3> S11> S15> S5. The available K was high in the order of S14>S3>S5>S6>S4>S7>S9 and medium in order of S15> S13> S12> S10> S1> S11> S8> S2. However, S5, S6, S7, S8, S10, S11, S12, S13 and S14 were found deficient in available S and remaining soil associations were found medium except S4 which was sufficient. It was observed that the available zinc content was low in the order of S6< S5< S9< S15< S10< S8. Only the soil association (S15) was found medium in available Fe and remaining soil associations were sufficient. In soils of Dindori district, the mean values of total N, P, K, S, Zn, Cu, Fe, Mn and B were analyzed to be 2111.57 kg ha-1, 142.64 mg kg -1, 7272.25 kg ha-1, 218.64 mg kg-1, 61.17 mg kg-1, 100.26 mg kg-1, 0.42%, 763.13 mg kg-1 and 55.63 mg kg-1, respectively. The geo-statistical results suggested that the exponential model best fitted for pH, ln N, P, ln K, ln S, ln Zn, Cu, ln Fe and B while spherical model best fitted for EC, OC, CaCO3 and Mn. The nugget/sill ratios of variogram models for OC, CaCO3, N, P, K, S, Cu, Fe, Mn and B ranged from 25.17% to 48.16%, which showed moderate spatial dependency. Soil pH showed significant positive correlation with OC, N, K and S and negative correlation with Fe. However, N, K and micronutrients content in soil exhibited a significant positive correlation with organic carbon. The micronutrients content in wheat were positive and significantly related with each other. Results of PCA showed the five components, which explained 66.83% of the total variance. PC1 (Zn, Cu, Fe, Mn and B), PC2 (OC, N and K), PC3 (CaCO3, EC), PC4 (pH and S) and PC5 (P) accounted for 20.12%, 15.14%, 12.62%, 11.12% and 7.84% variance of total variance, respectively.en_US
dc.identifier.urihttp://krishikosh.egranth.ac.in/handle/1/5810002661
dc.keywordsSOIL SCIENCE ,AGRICULTURAL CHEMISTRYen_US
dc.language.isoenen_US
dc.pages86en_US
dc.publisherJNKVVen_US
dc.subSoil Science and Agriculture Chemistryen_US
dc.subjectSOIL SCIENCE AND AGRICULTURAL CHEMISTRYen_US
dc.themeSOIL SCIENCE AND AGRICULTURAL CHEMISTRYen_US
dc.these.typeM.Scen_US
dc.titleAssessment of nutrient status and develop its blueprints using geo-statistical approach in soils of dindori districten_US
dc.typeThesisen_US
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