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Maize-based crop systems are promoted in large scale in South Asia because they are more sustainable and efficient than rice-based systems. In the present study, using two combinations of crop residue management practices (CRM) with four precision nitrogen (N) management (PNM) systems, we assessed the impacts on soil physicochemical characteristics [soil organic carbon (SOC), bulk density (BD), soil penetration resistance (PR)] and crop yields in 6 years old continuous zero tillage (ZT) practices under maize-wheat-mungbean cropping system in a sandy loam soil of northwestern India. The highest SOC (5.73 g/kg) was observed in Zero Tillage with Residue Retention (ZT + R) plots. Zero-tillage with residue retention (ZT + R) significantly reduced the bulk density over the zero-tillage with no residue retention (ZT-R) across the soil depth. The bulk density in ZT + R was 6.5 and 10.7% lower at 0–15 cm and 15–30 cm soil depth, respectively, than under ZT-R. The penetration resistance (PR) was significantly lower in ZT + R than in ZT-R across the soil depth. Soil organic carbon (SOC) in ZT + R was 7.4% higher at 0–15 cm depth and 11.9% higher at 15–30 cm depth than under ZT-R treatment. Among PNM treatments, the sequence of treatments in SOC content was 50%N + Green Seeker (GS) >33%N + GS > RDN > 70%N + GS. The system productivity (maize equivalent yield) under ZT + R in combination with 50%BN + GS was 15.0% higher than crops grown under ZT-R with RDN. The wheat equivalent yield under the ZT + R treatment is found to be higher (5.97) in the 50%BN + GS, which was 18% higher than the recommended dose of nitrogen treatment (5.04) and 28% higher than the 70%BN + GS treatment (4.68). Results demonstrated that plots with residue retention performed better, showing a 10% increase in system productivity. The study concludes that a ZT-based system with maize-based crop rotations (MWMb) with crop residue retention and precision nitrogen management can improve soil properties and system productivity in northwestern India.

A field experiment was conducted during the rainy (kharif) and winter (rabi) seasons of 2019–20 and 2020–21 at research farm of ICAR-Indian Agricultural Research Institute, New Delhi to study the effect of various crop establishment methods and microbial consortia (Pusa decomposer) mediated in situ rice straw management options on zinc (Zn) concentration and uptake in rice (Oryza sativa L.) (cv. Pusa Basmati 1509) and wheat (Triticum aestivum L.) (cv. HD 2967). The experiment was laid out in split-plot design (SPD) with 3 replications having 2 main plot (main factor) treatments, viz. aerobic rice (AR); and conventional transplanted (CT) rice in wet season; and 7 sub- plot (sub-factor) treatments, viz. Clean cultivation (removal of paddy straw); Paddy straw incorporation (PSI); Paddy straw mulching (PSM); PSI + Pusa decomposer (PD); PSM + PD; PSI + urea @20 kg/ha; and PSI + PD + urea @10 kg/ha in dry season. Findings showed that, in rice the zinc concentration (18.51 and 20.30 mg/kg in grain; 57.02 and 57.81 mg/kg in straw) and uptake (78.38 and 89.81 g/ha in grain; 427.1 and 434.6 g/ha in straw) were significantly superior in CT rice than AR in main plots during both the years of experiments. However, sub-plot treatments were non-significant in the zinc concentration and uptake. In wheat, among in situ rice straw management options (sub- plots), paddy straw incorporation + Pusa decomposer + Urea @10 kg/ha treatment significantly resulted in higher Zn concentration (38.08 and 39.03 mg/kg in grain; 28.24 and 29.01 mg/kg in straw) and uptake (185.0 and 191.9 g/ha in grain; 234.2 and 242.6 g/ha in straw) compared to other treatments and control (clean cultivation). The principal component analysis revealed that Zn uptake positively correlated with straw and grain yields in rice and wheat. Thus, the same treatment was found to be a better option for higher Zn concentration and uptake in the rice-wheat cropping system.

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.