Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3-[RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m−2 s−1), Tr (7.03 m mol H2O m−2 s−1), Gs (0.175 µmol CO2 mol−1 year−1), and Ci (271.7 mol H2O m2 s−1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09–4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha−1 m−3). However, economic water productivity (27.5 INR ha−1 m−3) and water-use efficiency (7.6 kg ha−1 mm−1 day−1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha−1) and biological yield (8.37 t ha−1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agro-ecological regions

A Novel Electrochemical Biosensor Based on Hematite (alpha-Fe2O3) Flowerlike Nanostructures for Sensitive Determination of Formaldehyde Adulteration in Fruit Juices

High-performance electrochemical enzymatic biosensor based on flowerlike alpha-Fe2O3 nanostructures was successfully developed for the detection of potential food adulterant, formaldehyde (formalin). The biosensor was found to be highly sensitive (744.15 mu A mg(-1) Lcm(-2)) with linear range of detection (0.01-0.3 mg/L) and showed high shelf-life (9 weeks) and precision (0.73% RSD) with reasonably good reproducibility. The biosensor application in real sample analysis was successfully accomplished using cyclic voltammetry (CV) technique. The developed biosensor exhibited detection limits of 0.02 mg/L and 0.04 mg/L in extracted and commercial orange juice samples, respectively, while 0.03 mg/L in extracted mango juice and 0.05 mg/L in commercial mango juice were obtained. The obtained detection limits are well below the maximum daily dose reference set by Environmental Protection Agency (EPA), USA, for formaldehyde. Biosensor results were found in good agreement with those obtained with HPLC (p < 0.05) and highlight market acceptability with usefulness and effectiveness of the proposed method for food quality and safety evaluation

Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models

Reliable and quick estimation of wheat yellow rust (WYR) severity in field is essential to manage the disease and minimize the losses. Field experiments were conducted during 2017–18 and 2018–19 to obtain visible and thermal images of 24 wheat cultivars having different levels of WYR resistance at critical growth stages. Machine learning (ML) models were constructed using the combinations of image indices (IN) and partial least square regression (PLS) scores of image indices with disease severity (DS) and Yeo-Johnson (YJ) transformed values of disease severity. The results revealed that 26 visible and 2 thermal indices considered in this study have significant correlations with WYR. The models performances were evaluated using four possible dataset combinations of (1) disease severity + indices, (2) disease severity + PLS scores of indices, (3) YJ transformed disease severity + indices, and (4) YJ transformed disease severity + PLS scores. Disease severity with image derived indices was found to be the best dataset for the prediction of WYR severity using machine learning models with an R2 and d-index above 0.95 during calibration, while up to 0.67 and 0.87, respectively during validation. Cubist model with disease severity + indices dataset was the best to predict WYR severity, while the Gaussian process regression with YJ transformed disease severity + PLS scores dataset was the poorest predictor. The results obtained in the present study showed the potential of ML models for non-destructive prediction of WYR in field using visible and thermal imaging

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Not AvailableIntergovernmental Panel on Climate Change (IPCC) promulgated a clear message that there have been many extreme weather and climate events observed globally since 1950, and these changes occurred mainly due to anthropogenic causes and emission of greenhouse gases. A computation study was carried out to assess the extreme temperature and rainfall events for the period 1984–2015 at the Indian Agricultural Research Institute, New Delhi by using ETCCDI indices through RClimDex software. The statistical significance of time series data and various calculated indices was done by linear regression as well as by Mann-Kendall test. Results indicated that annual mean maximum temperature decreased significantly at 0.019 °C/year and annual mean minimum temperature showed an increasing trend but without statistical significance. Alteration has happened in atmospheric properties, both physical and chemical over Delhi region during the period because of rapid urbanization and, increased concentration of aerosol. Fossil fuel/biomass waste burning, transportation of sand dust from Thar Desert, and reduction in incoming solar radiation have contributed both for fall in daytime temperature and rise in nighttime temperature. The changes in temperature would affect agricultural production through reduction in the rate of photosynthesis and excessive nocturnal respiration. Frequency and magnitude of coolest day (maximum temperature < 15 °C) and night (minimum temperature < 5 °C) have been rising at IARI, New Delhi. In the case of rainfall-based indices, annual rainfall (PRCPTOT), consecutive wet days (CWD), and number of days with rainfall ≥ 20 mm (R20) showed significant increasing tendency. Increasing trend in simple daily intensity index (SDII), rainy days (R2.5), and declining trend of consecutive dry days (CDD) indicates better distribution of rainfall. Nevertheless, increasing tendency in RX1day, RX5day, and R99p indicates possibilities of heavy rainfall events although the trend has been found insignificant.Not Availabl

Development of electrochemical biosensor based on CNT-Fe3O4 nanocomposite to determine formaldehyde adulteration in orange juice

An electrochemical biosensor was developed to determine formaldehyde (HCHO) adulteration commonly found in food. The current responses of various electrodes based on multiwalled carbon nanotubes (CNTs) and synthesized nanocomposite (CNT-Fe3O4) were measured using cyclic voltammetry. The nanocomposite based biosensor shows comparatively high sensitivity (527 mu Amg/L(-1)cm(-2)), low detection limit (0.05mg/L) in linear detection range 0.05-0.5mg/L for formaldehyde detection using formaldehyde dehydrogenase (FDH) enzyme. In real sample analysis, the low obtained RSD values (less than 1.79) and good recovery rates (more than 90%) signify an efficient and precise sensor for the selective quantification of formaldehyde in orange juice. The developed biosensor has future implications for determining formaldehyde adulteration in citrus fruit juices and other liquid foods in agri-food chain to further resolve global food safety concerns, control unethical business practices of adulteration and reduce the widespread food borne illness outbreaks

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Not AvailableThe sustainability of conventional rice (Oryza sativa L.) production systems is often questioned due to the over-mining of groundwater and environmental degradation. This has led to the development of cost-effective, resource-efficient, and environmentally clean rice production systems by optimizing water and nitrogen (N) use. Hence, a 2-year field study (2019 and 2020) was conducted at the ICAR–Indian Agricultural Research Institute, New Delhi, to assess the effect of precision N and water management strategies on growth, land, and water productivity, as well as energy-use efficiency in scented direct-seeded rice (DSR). Two crop establishment methods, conventional-till DSR (CT-DSR) and zero-till DSR (ZT-DSR) along with three irrigation scenarios (assured irrigation (irrigation after 72 h of the drying of surface water), irrigation at 20% depletion of available soil moisture (DASM), and 40% DASM+Si (80 kg ha−1 )) were assigned to the main plots; three N management options, a 100% recommended dose of N (RDN): 150 kg ha−1 ; Nutrient Expert®(NE®)+leaf color chart (LCC) and NE®+soil plant analysis development (SPAD) meter-based N management were allocated to sub-plots in a three-time replicated split-plot design. The CT-DSR produced 1.4, 11.8, and 89.4, and 2.4, 18.8, and 152.8% more grain yields, net returns, and net energy in 2019 and 2020, respectively, over ZT-DSR. However, ZT-DSR recorded 8.3 and 10.7% higher water productivity (WP) than CT-DSR. Assured irrigation resulted in 10.6, 16.1 16.9, and 8.1 and 12.3, 21.8 20.6, and 6.7% higher grain yields, net returns, net energy, and WP in 2019 and 2020, respectively, over irrigation at 20% DASM. Further, NE®+SPAD meter-based N management saved 27.1% N and recorded 9.6, 18.3, 16.8, and 8.3, and 8.8, 21.7, 19.9, and 10.7% greater grain yields, net returns, net energy, and WP over RDN in 2019 and 2020, respectively. Thus, the study suggested that the NE®+SPAD-based N application is beneficial over RDN for productivity, resource-use efficiency, and N-saving (~32 kg ha−1 ) both in CA-based and conventionally cultivated DSR. This study also suggests irrigating DSR after 72 h of the drying of surface water; however, under obviously limited water supplies, irrigation can be delayed until 20% DASM, thus saving two irrigations, which can be diverted to additional DSR areas.Not Availabl