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Precise application of nitrogenous fertilizers mainly urea ammonium nitrate (UAN) and granular urea (both pure and neem-coated) in aqueous form, is critically important for basal and foliar applications. The physicochemical properties of aqueous solution of these nitrogenous fertilizers plays important role in design of any liquid fertilizer application system. The study was carried out during 2019–20 at ICAR-Indian Agricultural Research Institute, New Delhi with an aim to develop algorithms for use in sensors-based systems for real time monitoring of available N concentration in the fertilizer solution before its application. The physicochemical properties like pH, electrical conductivity (EC), specific gravity (SG), dynamic viscosity (DV), surface tension (ST) and percentage light absorbance (LA) of urea ammonium nitrate (UAN-28%), pure urea, and neem coated urea in diluted form (pre-selected N-concentrations i.e. 0.78, 0.9, 1.08, 1.33, 1.75, 2.54 and 4.67% corresponding to the dilution ratios of 1:35, 1:30, 1:25, 1:20, 1:15, 1:10 and 1:05) were measured and analyzed for their significance with N-concentration. Electrical conductivity (EC) alone showed a significant relationship with N-concentrations. Prediction models were developed for diluted UAN and granular urea fertilizers based on the EC and N-concentrations. The regression models in terms of EC to predict N-concentration had coefficients of determination (R2) of 0.998, 0.998, and 0.999 for diluted UAN, pure urea, and neem coated urea with water, respectively showed a great potential for their sensor based precise application. The controlled application of diluted liquid fertilizers with water can be achieved through sensing relevant physicochemical property.

Abstract An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

The influence of modified clay mineral and red mud on biological yield and arsenic bioavailability to mustard were investigated. The total biomass of the mustard was increased by application of clay mineral and red mud products. At higher doses (5.00 g/kg), Fe-bentonite treated soil recorded the highest total plant biomass (11.2 g/pot). DMSO-bentonite (1.23 mg/kg) and Fe-bentonite (1.28 mg/kg) were proved to be most effective in reducing the extractable arsenic concentration in soil at the rate of 5.00 g/kg doses. These products also help in lowering the hazard quotient (0.20–0.08) values for human consumption of arsenic through mustard leaf. This means that modified types of bentonites and red mud boost mustard productivity by reducing arsenic concentration.

The release of nitrogen (N) from slow release N fertilizers (SRNF) is affected by soil microbial activity. However, several studies have shown variable results regarding the effect of microbial population size on the release of nutrient from slow release fertilizers. To understand this relationship clearly, changes in activities of two soil enzymes (urease and dehydrogenase), microbial population and available macronutrients viz nitrogen (N), phosphorus (P) and potassium (K) were assessed in an incubation study conducted under ambient condition. The six treatments were used for present investigation as control, 100% urea and crotonylidene diurea (CDU) @ 100, 75, 50 and 25% of recommended dose. The results indicated that the addition of N through commercially available urea and CDU as per general recommended dose of nutrients (GRDN) showed an increase in soil enzyme activity, microbial population, available N, P and K. The urease enzyme activity was found to be highest of nutrients in GRDN treatment at 60 days after addition (DAA) (39.55 μg NH4+-N/g soil/hr) and in 100% N through CDU (37.45 μg NH4+-N/g soil/hr) at 90 DAA. The soil dehydrogenase enzyme activity was significantly higher in GRDN at 7, 14, 21 and 30 DAA (1.32, 1.44, 1.56 and 1.68 μg TPF/g soil/hr, respectively). However, at 60 and 90 DAA the dehydrogenase enzyme activity was significantly higher in 100% N through CDU (1.89 and 1.63 μg TPF/g soil/hr, respectively). The soil available N content increased up to 60 DAA in all the treatments and it was significantly higher in GRDN followed by 100% N through CDU. However, it was significantly higher at all the periods of incubation over 75, 50, 25 and 100% N through CDU.

Field experiments were conducted to assess the soil test crop response (STCR) based fertilizer requirements for pearl millet-wheat sequence under integrated nutrient management (INM) under long term field experiment since 2003 at IARI, New Delhi. The results indicated that the STCR based fertilizer with 10 tonnes FYM/ha recorded highest mean grain (3.62 and 6.13 tonnes/ha) and stover/straw (7.0 and 8.01 tonnes/ha) yields of pearl millet (Pennisetum glaucum L. R. Br) and wheat (Triticum aestivum L.), which were 123.4 and 104.3 % higher in term of grain yield over control, respectively. STCR based fertilizer with 5 tonnes FYM/ha, STCR based chemical fertilizer and RDF treatments were statistically at par with respect to yield of pearl millet and wheat. Higher system grain yield (9.74 tonnes/ha) of pearl millet-wheat cropping system was recorded with STCR based integrated use of fertilizer with 10 tonnes FYM/ha. Nutrients uptake by pearl millet and wheat were significantly higher in STCR based fertilizer with 10 tonnes FYM/ha as compared to other treatments. Available N, P and K status in post-harvest soil was improved significantly with 20 tonnes FYM/ha alone as compared to other treatments but statistically at par with STCR based fertilizer with 10 tonnes FYM/ha. The long term study based on STCR clearly demonstrated that STCR based integrated use of fertilizer for targeting yield can produce targeted yield of crops and save the fertilizer without impairing soil fertility.