Planning, Designing and Analysis of Experiments relating to AICRP on Soil Test Crop Response Correlation

Abstract

The balanced application of fertilizer nutrients particularly the major nutrients, N P and K in optimum quantity, based on soil test and crop requirement is one of the most vital aspects for sustaining higher agricultural production. This requires the application of optimally balanced quantity of fertilizers in right proportion through correct method and time of application for a specific soil-crop-climate situation. It ensures increased quantity of produce, maintenance of soil productivity and the most efficient and judicious use of applied fertilizers. Thus in this content the soil fertility evolution and refined fertilizer prescription for sustained agricultural production is of great importance to any country of the world in general and farming community in particular. Hence the soils have to be tested precisely for their available nutrient status for making fertilizer recommendation based on crop response and economic circumstances. The determination of the amount of fertilizer that should be applied to a crop would be delightfully simple if a chemist could analyze the soil, and then use the analyses to measure the amount of plant nutrients in the soil and to calculate the amounts that should be applied to correct deficiencies. It is unfortunate that the determination of fertilizer requirements is not as simple as this. As every soil chemist knows, there are basic problems in interpreting soil test values in terms of nutrient availability to crops due to the interacting effects of other soil constituents, surface reactions, the changes that may occur in test values both laterally across farmers’ fields and vertically down the soil profile, and to all these factors may be added the uncertainties of weather, effects of crop variety, disease, pests etc. Any suggestion therefore that fertilizer requirement can be determined solely on the basis of a simple laboratory analysis of a few grams of soil, represents a vast oversimplification of a highly complex system. Nevertheless soil analysis can provide useful information on the effect that fertilizers are likely to have on yields, and it is important to use this information for the estimation of fertilizer requirements. Soil tests can provide a valuable piece of information and as such should be used in conjunction with such other information that is available for the estimation of fertilizer requirements. Soil test crop-response studies has been going on for a quite a long period of time both in India and abroad. the All India Coordinated Research Project on Soil Test Crop Response Correlation was initiated during the year 1967-68. Currently, STCR project is having seventeen cooperating centres. Under the STCR project multiple regression approach is being used to calculate the dose of nutrient (s) required to obtain the maximum yield of crops under given set of experimental conditions. It can further be used to calculate the economic dose of fertilizer nutrients by incorporating a constant factor i.e. per unit cost of input (fertilizer) in the original equation. In this approach yield is regressed with soil nutrients, fertilizer nutrients, their quadratic terms and the interaction term of soil and fertilizer nutrients. For this the following criteria should be fulfilled. (a) Soil test crop response calibration for economic yield of a crop is possible only when the response to added nutrients follow the law of diminishing returns. i.e. the signs of partial regression coefficients of linear, quadratic terms of nutrients and their interaction with available soil nutrients should in general be positive, negative and negative ( + , _ , _ ) respectively. (b) The coefficient of determination should be high. (c) The partial regression coefficients should be statistically significant. (d) The experiment should have sufficient design points i.e. the number of treatments should be at least two or more than the number of variables in the model. The above criterions are seldom fulfilled under the STCR project data. In such cases the optimum values of the nutrients cannot be derived or if they could be derived, they are either too high or too low. Keeping in view of the above problems and for better analysis of data, their interpretation and improvement in soil test calibration, the projector coordinator (STCR) Indian Institute of Soil Science, Bhopal, formally approached IASRI, New Delhi for collaboration. As large amount of data have also been gathered under the project, the creation of a database under the project was also solicited. Consequently a project entitled Planning, designing and analysis of experiments relating to AICRP on soil test crop response correlation was under taken at IASRI w.e.f. March 01, 2000 with the following objectives: (1) To improve the existing methodology for analysis of data of ongoing STCR experiments. (2) To carry out planning, design for the conduct of new set of experiments and subsequently to carry out the analysis of data. (3) To develop a database for the project. In this report, the first two chapters contain introduction and review of literature. In the third chapter Analytical techniques has been discussed along with a method, which has been developed at IASRI based on Response surface methodology has been discussed. In this method, the optimal values of N,P and K fertilizer nutrients can be derived if the soil test values for a particular site is available. Chapter four deals with designs for future STCR experimentation. In this, a number of designs have been proposed with different designs points, based on the requirements of STCR project, from designs of type (5 x 4 x 3), ( 4 x 4 x 3), (4 x 4 x 4) etc. Chapter five deals with results and discussion. Although we have received the data from a number of centres but due to pending query for discrepancies, only data of seven centres (totalling about 12 experiments) have been discussed in detail. The common result is that in almost all the cases the response surface methodology produced the stationery point as saddle points i.e. neither maxima nor minima. In such cases exploration of the response surface in the vicinity of the stationery point has been attempted. The optimal values of the fertilizer nutrients N, P and K obtained by Response Surface Methodology, has been found to be closely related to that obtained by Targeted yield approach adopted by the STCR project. Thus one could advocate for the adoption of the Targeted yield approach as has been tested by sound statistical system of Response Surface Methodology. A number of models have been tried for all the experiments but the models with 15 variables and 18 variables have been mostly found to be better. One model with 15 variables, which includes the interactions (FN x FP), (FN x FK) and (FP x FK) also gives higher values of R-Square. In some cases it was possible to find the optimum values from the Multiple Regression equations. Lastly, in Chapter six we have given the sketch of the database prepared for storing the STCR data. In this, number of queries can be prepared and the data can be retrieved.