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Excessive dependence on chemical fertilizers and ignorance to organic and microbial inputs under intensive cropping systems are the basic components of contemporary agriculture, which evolves several sustainability issues, such as degraded soil health and sub-optimal crop productivity. This scenario urges for integrated nutrient management approaches, such as microbes-mediated integrated plant nutrition for curtailing the high doses as chemical fertilizers. Rationally, experiment has been conducted in pigeonpea at ICAR-IARI, New Delhi, with the aim of identifying the appropriate nutrient management technique involving microbial and organic nutrient sources for improved rhizo-modulation, crop productivity, and soil bio-fertility. The randomized block-designed experiment consisted nine treatments viz. Control, Recommended dose of fertilizers (RDF), RDFC Microbial inoculants (MI), Vermicompost (VC), Farm Yard Manure (FYM), Leaf Compost (LC), VC C MI, FYM C MI, and LC C MI. Rhizobium spp., Pseudomonas spp., Bacillus spp., and Frateuria aurantia were used as seedinoculating microbes. The results indicated the significant response of integration following the trend VC C MI > FYM C MI > LC C MI > RDF C MI for various plant shoot-root growth attributes and soil microbial and enzymatic properties. FYM C MI significantly improved the water-stable aggregates (22%), mean weight diameter (1.13 mm), and geometric mean diameter (0.93 mm), soil organic carbon (SOC), SOC stock, and SOC sequestration. The chemical properties viz. available N, P, and K were significantly improved with VC C MI. The study summarizes that FYM C MI could result in better soil physico-chemical and biological properties and shootroot development; however; VC C MI could improve available nutrients in the soil and may enhance the growth of pigeonpea more effectively. The outcomes of the study are postulated as a viable and alternative solution for excessive chemical fertilizer-based nutrient management and would also promote the microbial consortia and organic manures-based agroindustries. This would add to the goal of sustainable agricultural development by producing quality crop produce, maintaining agro-biodiversity and making the soils fertile and healthy that would be a "gift to the society.". [ABSTRACT FROM AUTHOR]

Imbalanced and indiscriminate use of chemical fertilizers has been adversely influencing the quality of soil, environment, biodiversity and nutrient status in soil. Conjoint application of bio-inoculants (BI) with organic or inorganic sources of nutrients tweaks nutrient synchrony in soil and improves plant nutrition. With this backdrop an experiment was conducted at Indian Agricultural Research Institute, New Delhi during 2016–2018. The objectives were to identify the suitable combinations of BI-mediated nutrient sources for higher productivity and profitability in pigeon pea–wheat cropping system (PWCS). The nine pigeon pea treatments; four sole applications viz., recommended dose of fertilizers (RDF), vermicompost (VC), farm yard manure (FYM), leaf compost (LC) and four conjoint applications viz., RDF + BI, VC + BI, FYM + BI and LC + BI and one control were replicated thrice under randomized block design (RBD). However, in succeeding wheat, each of the treatments applied to pigeon pea was further allocated to two levels (50% and 100%) in factorial RBD. Findings exhibit that FYM + BI could result into higher equivalent-system grain productivity (10.4 and 10.8 t ha−1 during 1st and 2nd year, respectively) of PWCS. However, profitability parameters of PWCS were higher with the RDF + BI. Uptake of nutrients (NPK) was significantly higher with FYM + BI in pigeon pea and RDF + BI in wheat. Nutrient harvest index (NHI) did not vary significantly in both the crops. Conclusively, bio-inoculation is more productive and beneficial in general, while, over the various combinations, recommendation of FYM + BI combination could be more productive and sustainable. [ABSTRACT FROM AUTHOR]

The experiments were conducted to evaluate the effects of silicon (Si) and phosphorus (P) application on crop productivity, grain quality, water-use efficiency (WUE), and soil enzyme activity in aerobic rice (AR) at Indian Agricultural Research Institute, New Delhi, India. Four levels each of Si (0, 40, 80, and 120 kg Si ha–1) and P (0, 30, 60, and 90 kg P2O5 ha–1) were tested in a factorial randomized block design (FRBD) replicated thrice. The growth, yield and quality of AR were enhanced with increasing Si and P application rates and a similar trend was observed for WUE and soil enzyme activity. The highest grain yield of AR was recorded with 120 kg Si and 90 kg P2O5 ha–1 followed by 80 kg Si and 60 kg P2O5 ha–1 and the lowest in control. The grain, straw yield and water productivity increased by 10–40%, 5–30%, and 10.2–39%, respectively in different treatments over control. Though, all studied parameters showed increment with increasing dose of Si and P; however 60 kg P2O5 and 80 kg Si ha–1 were statistically superior to their other respective doses. In conclusion, supplementation of Si and P fertilizers substantially increased the AR productivity in Trans-Gangetic plains of India (Figure 1). [ABSTRACT FROM AUTHOR]

The effects of herbicides, alone application or in combination with insecticides, on performance of soybean [Glycine max (L.) Merrill] were examined at ICAR-Indian Agricultural Research Institute, New Delhi during rainy seasons of 2013 and 2014. Alone application of imazethapyr 10 SL @ 1.01/ha or in combination with indoxacarb 14.5 SC @ 300 ml/ha and rynaxypyr 20 SC @ 100 ml/ha lowered monocot and dicot weed count, reduced weed dry matter and increased weed control efficiency in comparison with other herbicide and insecticide applications, individual or in combinations. These results suggested that the imazethapyr can effectively control different categories of weeds, especially of monocots in soybean field. However, seed yield, net returns and B: C ratios were higher in the quizalofop ethyl 5 EC (1.01/ha) applied plots followed by combined application of rynaxypyr 20 SC (100 ml/ha) + quizalofop ethyl 5 EC (1.01/ha). [ABSTRACT FROM AUTHOR]

• The hyperspectral data used, is ultrahigh spatial resolution of 4 cm. • Estimation of leaf area index (LAI), leaf and canopy chlorophyll content (LCC, CCC). • The physical radiative transfer model PROSAIL is implemented as hybrid technique. • LAI and CCC had highest prediction accuracy using a hybrid approach. • Empirical multivariate regression showed the best performance for LCC retrieval. Accurate and timely estimation of crop biophysical variables is necessary for monitoring crop growth and implementing effective nutrient management practices. Incorporating machine learning multivariate models with UAV-based hyperspectral imaging provides a fast non-destructive and near real-time prediction of these variables. In the present study, the hyperspectral data in the spectral range of 400–1000 nm from an imaging spectrometer integrated into an unmanned aerial vehicle (UAV) was used for mapping experimental fields of wheat crop in ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi. The imaging spectroradiometer has a spectral resolution of 2.2 nm with 269 distinct bands and imaging with an ultrahigh spatial resolution of 4 cm was employed for the experiment. Five competitive machine learning algorithms, i.e., artificial neural network (ANN), extreme learning machine (ELM), multivariate adaptive regression spline (MARS), random forest (RF), and support vector machine (SVM) were evaluated for predicting biophysical variables of the wheat crop, namely leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The physical radiative transfer model (RTM) PROSAIL was also applied in combination with a machine learning regression algorithms toolbox as available in the automated radiative transfer model's operator (ARTMO) software, leading to hybrid models. In the empirical analysis, ELM outperformed the other algorithms with maximum validation R2 of 0.948, 0.990, and 0.963 for LAI, LCC, and CCC, respectively. However, in the case of hybrid modelling, on validated against simulated data ANN outperforms the other models with maximum validation R2 of 0.983, 0.969, and 0.998 for LAI, LCC, and CCC, respectively. Validated against real data, the NRMSE values obtained for LAI, LCC, and CCC retrieved maps are 24.51 %, 38.74 %, and 36.16 %, respectively. The accurate retrieval of LAI and CCC with the highest prediction accuracy was obtained using a hybrid approach, while empirical multivariate regression applied to image spectra showed the best performance for LCC mapping. The study provides feasible solutions to infer the state of croplands in support of better farm management practices. [ABSTRACT FROM AUTHOR]